{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# My Energy Use Data Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plots can also be found on Plotly and on my website <a href='http://www.thetrainingset.com'>The Training Set</a>.  The data are my hourly electricity demand made available by BGE and Opower using the Green Button protocol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Pandas Version:  0.15.2\n",
      "Numpy Version:  1.9.2\n",
      "Matplotlib Version:  1.4.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'import_funcs' from 'import_funcs.pyc'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print '\\nPandas Version: ',pd.__version__\n",
    "\n",
    "import numpy as np\n",
    "print 'Numpy Version: ', np.__version__\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "print 'Matplotlib Version: ', matplotlib.__version__\n",
    "\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *\n",
    "#print 'Plot.ly Version: ', py.__version__\n",
    "\n",
    "# My import functions to get a clean Pandas dataframe\n",
    "import import_funcs\n",
    "reload(import_funcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Pretty colors\n",
    "gray_light = '#bbbbbb'\n",
    "gray_med = '#737373'\n",
    "red_orange = '#ff3700'\n",
    "my_cyan = '#00CCFF'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COST</th>\n",
       "      <th>NOTES</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>UNITS</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>timestamp_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-31 19:00:00</th>\n",
       "      <td> $0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td> Electric usage</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 0.62</td>\n",
       "      <td>2015-10-31 19:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31 20:00:00</th>\n",
       "      <td> $0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td> Electric usage</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 0.28</td>\n",
       "      <td>2015-10-31 20:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31 21:00:00</th>\n",
       "      <td> $0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td> Electric usage</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 0.09</td>\n",
       "      <td>2015-10-31 21:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31 22:00:00</th>\n",
       "      <td> $0.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td> Electric usage</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 0.78</td>\n",
       "      <td>2015-10-31 22:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31 23:00:00</th>\n",
       "      <td> $0.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td> Electric usage</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 1.29</td>\n",
       "      <td>2015-10-31 23:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      COST  NOTES            TYPE UNITS  USAGE  \\\n",
       "timestamp                                                        \n",
       "2015-10-31 19:00:00  $0.09    NaN  Electric usage   kWh   0.62   \n",
       "2015-10-31 20:00:00  $0.04    NaN  Electric usage   kWh   0.28   \n",
       "2015-10-31 21:00:00  $0.01    NaN  Electric usage   kWh   0.09   \n",
       "2015-10-31 22:00:00  $0.11    NaN  Electric usage   kWh   0.78   \n",
       "2015-10-31 23:00:00  $0.18    NaN  Electric usage   kWh   1.29   \n",
       "\n",
       "                          timestamp_end  \n",
       "timestamp                                \n",
       "2015-10-31 19:00:00 2015-10-31 19:59:00  \n",
       "2015-10-31 20:00:00 2015-10-31 20:59:00  \n",
       "2015-10-31 21:00:00 2015-10-31 21:59:00  \n",
       "2015-10-31 22:00:00 2015-10-31 22:59:00  \n",
       "2015-10-31 23:00:00 2015-10-31 23:59:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the BGE hourly electricity data and weather data using import_funcs.py class\n",
    "#old_apt = import_funcs.BGEdata(\"old apt\")\n",
    "new_apt = import_funcs.BGEdata(\"new apt\")\n",
    "#weather = import_funcs.weather()\n",
    "\n",
    "# Merge into one Pandas dataframe\n",
    "#old_apt_and_weather = pd.merge(weather,old_apt,left_index=True,right_index=True)\n",
    "#new_apt_and_weather = pd.merge(weather,new_apt,left_index=True,right_index=True)\n",
    "#new_apt_and_weather = new_apt\n",
    "\n",
    "new_apt.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hourly for Each Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0.26</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.73</td>\n",
       "      <td> 2.00</td>\n",
       "      <td> 0.55</td>\n",
       "      <td> 0.08</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.12</td>\n",
       "      <td> 0.92</td>\n",
       "      <td> 2.00</td>\n",
       "      <td> 1.61</td>\n",
       "      <td> 0.85</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-28</th>\n",
       "      <td> 0.14</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 1.90</td>\n",
       "      <td> 3.28</td>\n",
       "      <td> 0.67</td>\n",
       "      <td> 0.38</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.45</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0.50</td>\n",
       "      <td> 0.93</td>\n",
       "      <td> 0.95</td>\n",
       "      <td> 0.83</td>\n",
       "      <td> 0.86</td>\n",
       "      <td> 0.81</td>\n",
       "      <td> 0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-29</th>\n",
       "      <td> 0.12</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0.46</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 1.79</td>\n",
       "      <td> 1.09</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.09</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.45</td>\n",
       "      <td> 0.34</td>\n",
       "      <td> 2.60</td>\n",
       "      <td> 4.53</td>\n",
       "      <td> 1.39</td>\n",
       "      <td> 0.59</td>\n",
       "      <td> 0.16</td>\n",
       "      <td> 0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-30</th>\n",
       "      <td> 0.41</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 1.04</td>\n",
       "      <td> 2.31</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0.21</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.44</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.84</td>\n",
       "      <td> 0.93</td>\n",
       "      <td> 0.80</td>\n",
       "      <td> 0.73</td>\n",
       "      <td> 1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td> 0.23</td>\n",
       "      <td> 0.19</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0.18</td>\n",
       "      <td> 0.18</td>\n",
       "      <td> 0.21</td>\n",
       "      <td> 0.17</td>\n",
       "      <td> 0.20</td>\n",
       "      <td> 0.66</td>\n",
       "      <td> 1.76</td>\n",
       "      <td>...</td>\n",
       "      <td> 0.73</td>\n",
       "      <td> 0.79</td>\n",
       "      <td> 0.19</td>\n",
       "      <td> 1.26</td>\n",
       "      <td> 0.40</td>\n",
       "      <td> 0.62</td>\n",
       "      <td> 0.28</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.78</td>\n",
       "      <td> 1.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     1   2     3     4     5     6     7     8     9   ...   \\\n",
       "timestamp                                                             ...    \n",
       "2015-10-27  0.11  0.26   1  0.09  0.09  0.09  0.73  2.00  0.55  0.08  ...    \n",
       "2015-10-28  0.14  0.11   1  0.11  0.08  0.11  1.90  3.28  0.67  0.38  ...    \n",
       "2015-10-29  0.12  0.10   1  0.46  0.09  0.09  1.79  1.09  0.08  0.09  ...    \n",
       "2015-10-30  0.41  0.09   1  0.09  0.09  0.10  1.04  2.31  0.10  0.21  ...    \n",
       "2015-10-31  0.23  0.19   0  0.18  0.18  0.21  0.17  0.20  0.66  1.76  ...    \n",
       "\n",
       "              14    15    16    17    18    19    20    21    22    23  \n",
       "timestamp                                                               \n",
       "2015-10-27  0.08  0.08  0.08  0.12  0.92  2.00  1.61  0.85  0.10  0.48  \n",
       "2015-10-28  0.08  0.45  0.11  0.50  0.93  0.95  0.83  0.86  0.81  0.85  \n",
       "2015-10-29  0.09  0.09  0.45  0.34  2.60  4.53  1.39  0.59  0.16  0.10  \n",
       "2015-10-30  0.08  0.44  0.08  0.08  0.08  0.84  0.93  0.80  0.73  1.01  \n",
       "2015-10-31  0.73  0.79  0.19  1.26  0.40  0.62  0.28  0.09  0.78  1.29  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build hourly_array\n",
    "# Rows: each day\n",
    "# Cols: each hour\n",
    "\n",
    "hourly_array = pd.DataFrame(np.zeros([len(new_apt['USAGE'].resample('d',how='sum')),24]),index=new_apt['USAGE'].resample('d',how='sum').index)\n",
    "\n",
    "for i in range(0,24):\n",
    "    if len(hourly_array[i]) == len(new_apt['USAGE'].ix[new_apt.index.hour==i]):\n",
    "        hourly_array[i] = np.array(new_apt['USAGE'][new_apt.index.hour==i])\n",
    "    #hourly_array[i] = np.array(new_apt['USAGE'][new_apt.index.hour==i])\n",
    "    else:\n",
    "        for j in range(0,len(new_apt['USAGE'].ix[new_apt.index.hour==i])):\n",
    "            hourly_array[i].ix[j] = 1\n",
    "            \n",
    "            ## FIX THIS\n",
    "            #new_apt['USAGE'].ix[new_apt['USAGE'].ix[pd.to_datetime[hourly_array.index]]]\n",
    "                                                        \n",
    "hourly_array.sort_index(ascending=True, inplace=True)\n",
    "hourly_array.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COST                             NaN\n",
       "NOTES                            NaN\n",
       "TYPE                  Electric usage\n",
       "UNITS                            kWh\n",
       "USAGE                           0.04\n",
       "timestamp_end    2014-05-24 00:59:00\n",
       "Name: 2014-05-24 00:00:00, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_apt.ix[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/figure.py:1644: UserWarning:\n",
      "\n",
      "This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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+py72cuDGxoYRsQHYIGkmcAxwctFOP3PqiYMYgpmZjYSJlGivBU6WdAJwA1lB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vtmzMyGq17HaCWtB+wHHBwR1xThx4Fbas5rA74GfIBUAd8DnBgRVxbHZwF/Av4BOBXYvjjnUxFx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\nuTA+ub2+9EBENobCE627H7Gex28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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1081277d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "sundays = pd.Series(hourly_array[hourly_array.index.dayofweek==0].index)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[10,25])\n",
    "title('My Electricity Usage \\n', fontsize=18)\n",
    "plt.imshow(hourly_array, interpolation='none', cmap='YlGnBu')\n",
    "plt.tick_params(axis=\"both\", which=\"both\", bottom=\"on\", top=\"on\",\n",
    "                labelbottom=\"on\", labeltop='on', left=\"on\", right=\"off\", labelleft=\"on\") \n",
    "colorbar_ax = fig.add_axes([0.8, .375, .04, .25])\n",
    "cb = plt.colorbar(cax = colorbar_ax)\n",
    "cb.set_label('kilowatt-hours',fontsize = 16)\n",
    "\n",
    "xticks = range(0,24,4)\n",
    "yticks = range(0,len(hourly_array),7)\n",
    "\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_yticklabels(sundays.apply(lambda x: x.strftime('%B %d')), fontsize=14)\n",
    "ax.yaxis_date()\n",
    "\n",
    "#ax.yaxis.set_major_formatter(mdates.DateFormatter('%Y')) This didn't work for me\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#fig.savefig('my_usage_heatmap.png', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-05-24</th>\n",
       "      <td> 0.04</td>\n",
       "      <td> 0.07</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-25</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.07</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-26</th>\n",
       "      <td> 0.15</td>\n",
       "      <td> 0.13</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-27</th>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-28</th>\n",
       "      <td> 1.53</td>\n",
       "      <td> 1.44</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-29</th>\n",
       "      <td> 0.47</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-30</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-01</th>\n",
       "      <td> 0.12</td>\n",
       "      <td> 0.12</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-02</th>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-03</th>\n",
       "      <td> 1.04</td>\n",
       "      <td> 0.69</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-04</th>\n",
       "      <td> 1.12</td>\n",
       "      <td> 0.82</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-05</th>\n",
       "      <td> 1.22</td>\n",
       "      <td> 0.99</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-06</th>\n",
       "      <td> 0.78</td>\n",
       "      <td> 0.51</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-07</th>\n",
       "      <td> 0.28</td>\n",
       "      <td> 0.28</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-08</th>\n",
       "      <td> 0.64</td>\n",
       "      <td> 0.54</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-09</th>\n",
       "      <td> 1.28</td>\n",
       "      <td> 0.81</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-10</th>\n",
       "      <td> 0.54</td>\n",
       "      <td> 0.92</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-11</th>\n",
       "      <td> 0.88</td>\n",
       "      <td> 0.71</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-12</th>\n",
       "      <td> 1.22</td>\n",
       "      <td> 1.38</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-13</th>\n",
       "      <td> 0.08</td>\n",
       "      <td> 1.73</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-14</th>\n",
       "      <td> 0.78</td>\n",
       "      <td> 0.93</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15</th>\n",
       "      <td> 0.79</td>\n",
       "      <td> 0.32</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-16</th>\n",
       "      <td> 0.45</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-17</th>\n",
       "      <td> 0.81</td>\n",
       "      <td> 0.57</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-18</th>\n",
       "      <td> 1.27</td>\n",
       "      <td> 1.42</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-19</th>\n",
       "      <td> 1.46</td>\n",
       "      <td> 1.32</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-20</th>\n",
       "      <td> 1.10</td>\n",
       "      <td> 0.85</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-21</th>\n",
       "      <td> 2.07</td>\n",
       "      <td> 1.33</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-22</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.47</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-02</th>\n",
       "      <td> 0.19</td>\n",
       "      <td> 0.19</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-03</th>\n",
       "      <td> 0.46</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-04</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-05</th>\n",
       "      <td> 0.44</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-06</th>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-07</th>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-08</th>\n",
       "      <td> 0.42</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-09</th>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-10</th>\n",
       "      <td> 0.31</td>\n",
       "      <td> 0.26</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-11</th>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-12</th>\n",
       "      <td> 0.42</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-13</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-14</th>\n",
       "      <td> 0.16</td>\n",
       "      <td> 0.21</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-15</th>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-16</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-17</th>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-18</th>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0.05</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-19</th>\n",
       "      <td> 0.07</td>\n",
       "      <td> 0.05</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-20</th>\n",
       "      <td> 0.54</td>\n",
       "      <td> 0.07</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-21</th>\n",
       "      <td> 0.06</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-22</th>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-23</th>\n",
       "      <td> 0.49</td>\n",
       "      <td> 0.22</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-24</th>\n",
       "      <td> 0.22</td>\n",
       "      <td> 0.22</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-25</th>\n",
       "      <td> 0.43</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-26</th>\n",
       "      <td> 0.40</td>\n",
       "      <td> 0.08</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0.26</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-28</th>\n",
       "      <td> 0.14</td>\n",
       "      <td> 0.11</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-29</th>\n",
       "      <td> 0.12</td>\n",
       "      <td> 0.10</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-30</th>\n",
       "      <td> 0.41</td>\n",
       "      <td> 0.09</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td> 0.23</td>\n",
       "      <td> 0.19</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     1   2   3   4   5   6   7   8   9  ...  14  15  16  17  \\\n",
       "timestamp                                              ...                   \n",
       "2014-05-24  0.04  0.07   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-25  0.09  0.07   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-26  0.15  0.13   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-27  0.08  0.08   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-28  1.53  1.44   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-29  0.47  0.10   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-30  0.09  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-05-31  0.08  0.08   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-01  0.12  0.12   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-02  0.11  0.11   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-03  1.04  0.69   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-04  1.12  0.82   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-05  1.22  0.99   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-06  0.78  0.51   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-07  0.28  0.28   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-08  0.64  0.54   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-09  1.28  0.81   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-10  0.54  0.92   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-11  0.88  0.71   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-12  1.22  1.38   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-13  0.08  1.73   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-14  0.78  0.93   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-15  0.79  0.32   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-16  0.45  0.11   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-17  0.81  0.57   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-18  1.27  1.42   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-19  1.46  1.32   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-20  1.10  0.85   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-21  2.07  1.33   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2014-06-22  0.09  0.47   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "...          ...   ...  ..  ..  ..  ..  ..  ..  ..  .. ...  ..  ..  ..  ..   \n",
       "2015-10-02  0.19  0.19   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-03  0.46  0.10   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-04  0.09  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-05  0.44  0.11   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-06  0.10  0.10   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-07  0.10  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-08  0.42  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-09  0.10  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-10  0.31  0.26   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-11  0.08  0.08   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-12  0.42  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-13  0.09  0.10   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-14  0.16  0.21   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-15  0.08  0.08   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-16  0.09  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-17  0.09  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-18  0.08  0.05   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-19  0.07  0.05   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-20  0.54  0.07   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-21  0.06  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-22  0.10  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-23  0.49  0.22   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-24  0.22  0.22   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-25  0.43  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-26  0.40  0.08   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-27  0.11  0.26   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-28  0.14  0.11   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-29  0.12  0.10   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-30  0.41  0.09   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "2015-10-31  0.23  0.19   0   0   0   0   0   0   0   0 ...   0   0   0   0   \n",
       "\n",
       "            18  19  20  21  22  23  \n",
       "timestamp                           \n",
       "2014-05-24   0   0   0   0   0   0  \n",
       "2014-05-25   0   0   0   0   0   0  \n",
       "2014-05-26   0   0   0   0   0   0  \n",
       "2014-05-27   0   0   0   0   0   0  \n",
       "2014-05-28   0   0   0   0   0   0  \n",
       "2014-05-29   0   0   0   0   0   0  \n",
       "2014-05-30   0   0   0   0   0   0  \n",
       "2014-05-31   0   0   0   0   0   0  \n",
       "2014-06-01   0   0   0   0   0   0  \n",
       "2014-06-02   0   0   0   0   0   0  \n",
       "2014-06-03   0   0   0   0   0   0  \n",
       "2014-06-04   0   0   0   0   0   0  \n",
       "2014-06-05   0   0   0   0   0   0  \n",
       "2014-06-06   0   0   0   0   0   0  \n",
       "2014-06-07   0   0   0   0   0   0  \n",
       "2014-06-08   0   0   0   0   0   0  \n",
       "2014-06-09   0   0   0   0   0   0  \n",
       "2014-06-10   0   0   0   0   0   0  \n",
       "2014-06-11   0   0   0   0   0   0  \n",
       "2014-06-12   0   0   0   0   0   0  \n",
       "2014-06-13   0   0   0   0   0   0  \n",
       "2014-06-14   0   0   0   0   0   0  \n",
       "2014-06-15   0   0   0   0   0   0  \n",
       "2014-06-16   0   0   0   0   0   0  \n",
       "2014-06-17   0   0   0   0   0   0  \n",
       "2014-06-18   0   0   0   0   0   0  \n",
       "2014-06-19   0   0   0   0   0   0  \n",
       "2014-06-20   0   0   0   0   0   0  \n",
       "2014-06-21   0   0   0   0   0   0  \n",
       "2014-06-22   0   0   0   0   0   0  \n",
       "...         ..  ..  ..  ..  ..  ..  \n",
       "2015-10-02   0   0   0   0   0   0  \n",
       "2015-10-03   0   0   0   0   0   0  \n",
       "2015-10-04   0   0   0   0   0   0  \n",
       "2015-10-05   0   0   0   0   0   0  \n",
       "2015-10-06   0   0   0   0   0   0  \n",
       "2015-10-07   0   0   0   0   0   0  \n",
       "2015-10-08   0   0   0   0   0   0  \n",
       "2015-10-09   0   0   0   0   0   0  \n",
       "2015-10-10   0   0   0   0   0   0  \n",
       "2015-10-11   0   0   0   0   0   0  \n",
       "2015-10-12   0   0   0   0   0   0  \n",
       "2015-10-13   0   0   0   0   0   0  \n",
       "2015-10-14   0   0   0   0   0   0  \n",
       "2015-10-15   0   0   0   0   0   0  \n",
       "2015-10-16   0   0   0   0   0   0  \n",
       "2015-10-17   0   0   0   0   0   0  \n",
       "2015-10-18   0   0   0   0   0   0  \n",
       "2015-10-19   0   0   0   0   0   0  \n",
       "2015-10-20   0   0   0   0   0   0  \n",
       "2015-10-21   0   0   0   0   0   0  \n",
       "2015-10-22   0   0   0   0   0   0  \n",
       "2015-10-23   0   0   0   0   0   0  \n",
       "2015-10-24   0   0   0   0   0   0  \n",
       "2015-10-25   0   0   0   0   0   0  \n",
       "2015-10-26   0   0   0   0   0   0  \n",
       "2015-10-27   0   0   0   0   0   0  \n",
       "2015-10-28   0   0   0   0   0   0  \n",
       "2015-10-29   0   0   0   0   0   0  \n",
       "2015-10-30   0   0   0   0   0   0  \n",
       "2015-10-31   0   0   0   0   0   0  \n",
       "\n",
       "[526 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To Plot.ly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SSLError",
     "evalue": "[Errno 8] _ssl.c:507: EOF occurred in violation of protocol",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mSSLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-64a93d088504>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m )\n\u001b[1;32m     23\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'My Elec Use Heat Map'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;31m#fig = Figure(data=data, layout=layout)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m#plot_url = py.plot(fig, filename='YIGnBu-heatmap')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[0;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'auto_open'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'auto_open'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m     \u001b[0murlsplit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0musername\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlsplit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murlsplit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# TODO: HACKY!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[1;32m    212\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mplot_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'auto_open'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[0;34m(figure, **plot_options)\u001b[0m\n\u001b[1;32m   1229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m     r = requests.post(url, data=payload,\n\u001b[0;32m-> 1231\u001b[0;31m                       verify=get_config()['plotly_ssl_verification'])\n\u001b[0m\u001b[1;32m   1232\u001b[0m     \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1233\u001b[0m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/requests/api.pyc\u001b[0m in \u001b[0;36mpost\u001b[0;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \"\"\"\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'post'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/requests/api.pyc\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m     \u001b[0;31m# By explicitly closing the session, we avoid leaving sockets open which\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;31m# can trigger a ResourceWarning in some cases, and look like a memory leak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/requests/sessions.pyc\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    459\u001b[0m         }\n\u001b[1;32m    460\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/requests/sessions.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/requests/adapters.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    429\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_SSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_HTTPError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_SSLError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 431\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mReadTimeoutError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mReadTimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSSLError\u001b[0m: [Errno 8] _ssl.c:507: EOF occurred in violation of protocol"
     ]
    }
   ],
   "source": [
    "height = 1200\n",
    "width = 400\n",
    "\n",
    "data = Data([\n",
    "    Heatmap(\n",
    "        x = range(0,24,1),\n",
    "        y = hourly_array.index,\n",
    "        z = np.array(hourly_array),\n",
    "        colorscale='YIGnBu',\n",
    "        reversescale=True,\n",
    "        colorbar = ColorBar(\n",
    "            title = 'kWh',\n",
    "            titleside='right')\n",
    "    )\n",
    "])\n",
    "layout = Layout(\n",
    "    yaxis = YAxis(autorange='reversed'),\n",
    "    autosize = False,s\n",
    "    width=width,\n",
    "    height=height,\n",
    "    font = Font(size=18)\n",
    ")\n",
    "fig = Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='My Elec Use Heat Map')\n",
    "#fig = Figure(data=data, layout=layout)\n",
    "#plot_url = py.plot(fig, filename='YIGnBu-heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Daily Total by Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tues</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thurs</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-05-26</th>\n",
       "      <td> 12.46</td>\n",
       "      <td> 25.68</td>\n",
       "      <td> 17.00</td>\n",
       "      <td>  7.48</td>\n",
       "      <td>  8.24</td>\n",
       "      <td>  9.50</td>\n",
       "      <td>  8.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-02</th>\n",
       "      <td> 12.09</td>\n",
       "      <td> 16.34</td>\n",
       "      <td> 16.03</td>\n",
       "      <td> 12.65</td>\n",
       "      <td> 12.88</td>\n",
       "      <td> 34.12</td>\n",
       "      <td> 22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-09</th>\n",
       "      <td> 33.98</td>\n",
       "      <td> 16.52</td>\n",
       "      <td> 24.09</td>\n",
       "      <td> 10.20</td>\n",
       "      <td> 33.92</td>\n",
       "      <td> 16.81</td>\n",
       "      <td> 22.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-16</th>\n",
       "      <td> 15.07</td>\n",
       "      <td> 22.25</td>\n",
       "      <td> 26.37</td>\n",
       "      <td> 25.47</td>\n",
       "      <td> 10.69</td>\n",
       "      <td> 26.03</td>\n",
       "      <td> 29.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-23</th>\n",
       "      <td> 20.96</td>\n",
       "      <td> 26.21</td>\n",
       "      <td> 18.13</td>\n",
       "      <td> 22.89</td>\n",
       "      <td> 19.40</td>\n",
       "      <td> 32.24</td>\n",
       "      <td> 28.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td> 20.47</td>\n",
       "      <td> 19.01</td>\n",
       "      <td> 30.58</td>\n",
       "      <td> 19.25</td>\n",
       "      <td> 33.93</td>\n",
       "      <td> 17.85</td>\n",
       "      <td> 30.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td> 19.01</td>\n",
       "      <td> 22.15</td>\n",
       "      <td> 21.66</td>\n",
       "      <td> 20.65</td>\n",
       "      <td> 15.48</td>\n",
       "      <td> 31.25</td>\n",
       "      <td> 40.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-14</th>\n",
       "      <td> 22.37</td>\n",
       "      <td> 20.21</td>\n",
       "      <td> 19.83</td>\n",
       "      <td> 16.44</td>\n",
       "      <td> 16.78</td>\n",
       "      <td> 29.20</td>\n",
       "      <td> 18.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-21</th>\n",
       "      <td> 14.89</td>\n",
       "      <td> 19.33</td>\n",
       "      <td> 21.03</td>\n",
       "      <td> 17.69</td>\n",
       "      <td> 16.90</td>\n",
       "      <td> 23.03</td>\n",
       "      <td> 40.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td> 19.52</td>\n",
       "      <td> 11.83</td>\n",
       "      <td>  8.39</td>\n",
       "      <td> 13.73</td>\n",
       "      <td> 13.94</td>\n",
       "      <td> 27.80</td>\n",
       "      <td> 19.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-04</th>\n",
       "      <td> 21.07</td>\n",
       "      <td> 20.46</td>\n",
       "      <td> 15.31</td>\n",
       "      <td> 14.65</td>\n",
       "      <td> 21.77</td>\n",
       "      <td> 15.85</td>\n",
       "      <td> 18.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-11</th>\n",
       "      <td> 15.72</td>\n",
       "      <td> 20.31</td>\n",
       "      <td> 18.68</td>\n",
       "      <td>  9.39</td>\n",
       "      <td> 10.02</td>\n",
       "      <td> 14.92</td>\n",
       "      <td> 15.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-18</th>\n",
       "      <td> 22.83</td>\n",
       "      <td> 22.03</td>\n",
       "      <td> 16.18</td>\n",
       "      <td> 24.94</td>\n",
       "      <td> 16.29</td>\n",
       "      <td> 27.40</td>\n",
       "      <td> 28.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-25</th>\n",
       "      <td> 12.96</td>\n",
       "      <td> 15.39</td>\n",
       "      <td> 16.83</td>\n",
       "      <td> 20.14</td>\n",
       "      <td>  9.15</td>\n",
       "      <td> 12.12</td>\n",
       "      <td>  3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-01</th>\n",
       "      <td> 13.31</td>\n",
       "      <td> 29.63</td>\n",
       "      <td> 22.64</td>\n",
       "      <td> 16.16</td>\n",
       "      <td> 22.15</td>\n",
       "      <td> 35.00</td>\n",
       "      <td> 32.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-08</th>\n",
       "      <td> 15.58</td>\n",
       "      <td> 14.94</td>\n",
       "      <td> 18.09</td>\n",
       "      <td> 16.67</td>\n",
       "      <td> 13.56</td>\n",
       "      <td>  4.78</td>\n",
       "      <td>  3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-15</th>\n",
       "      <td> 15.21</td>\n",
       "      <td> 13.42</td>\n",
       "      <td> 10.42</td>\n",
       "      <td>  7.58</td>\n",
       "      <td>  6.64</td>\n",
       "      <td>  8.94</td>\n",
       "      <td> 13.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-22</th>\n",
       "      <td>  9.22</td>\n",
       "      <td> 10.93</td>\n",
       "      <td>  8.64</td>\n",
       "      <td>  8.99</td>\n",
       "      <td>  8.53</td>\n",
       "      <td>  8.66</td>\n",
       "      <td>  8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-29</th>\n",
       "      <td> 10.21</td>\n",
       "      <td>  6.29</td>\n",
       "      <td>  9.53</td>\n",
       "      <td>  6.73</td>\n",
       "      <td> 10.60</td>\n",
       "      <td>  6.82</td>\n",
       "      <td> 11.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-06</th>\n",
       "      <td>  8.87</td>\n",
       "      <td>  8.37</td>\n",
       "      <td>  7.84</td>\n",
       "      <td>  9.87</td>\n",
       "      <td>  8.19</td>\n",
       "      <td> 22.09</td>\n",
       "      <td>  7.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-13</th>\n",
       "      <td> 17.24</td>\n",
       "      <td>  7.72</td>\n",
       "      <td>  8.20</td>\n",
       "      <td> 15.70</td>\n",
       "      <td>  8.92</td>\n",
       "      <td> 15.78</td>\n",
       "      <td> 11.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-20</th>\n",
       "      <td>  6.30</td>\n",
       "      <td> 11.39</td>\n",
       "      <td>  9.34</td>\n",
       "      <td> 10.65</td>\n",
       "      <td> 16.50</td>\n",
       "      <td>  9.56</td>\n",
       "      <td> 17.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-27</th>\n",
       "      <td> 16.54</td>\n",
       "      <td>  7.53</td>\n",
       "      <td>  9.55</td>\n",
       "      <td>  9.03</td>\n",
       "      <td>  9.69</td>\n",
       "      <td> 19.86</td>\n",
       "      <td> 23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td> 12.90</td>\n",
       "      <td> 10.81</td>\n",
       "      <td>  9.09</td>\n",
       "      <td> 12.06</td>\n",
       "      <td>  8.99</td>\n",
       "      <td> 22.27</td>\n",
       "      <td> 12.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-10</th>\n",
       "      <td> 11.29</td>\n",
       "      <td> 12.74</td>\n",
       "      <td> 11.47</td>\n",
       "      <td> 12.24</td>\n",
       "      <td> 11.85</td>\n",
       "      <td> 18.96</td>\n",
       "      <td> 22.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-17</th>\n",
       "      <td> 17.24</td>\n",
       "      <td> 14.00</td>\n",
       "      <td> 16.85</td>\n",
       "      <td> 26.90</td>\n",
       "      <td> 13.49</td>\n",
       "      <td> 12.97</td>\n",
       "      <td> 20.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-24</th>\n",
       "      <td>  8.07</td>\n",
       "      <td>  9.58</td>\n",
       "      <td> 13.31</td>\n",
       "      <td> 21.95</td>\n",
       "      <td> 18.30</td>\n",
       "      <td> 26.41</td>\n",
       "      <td> 22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>  7.94</td>\n",
       "      <td> 12.12</td>\n",
       "      <td> 13.96</td>\n",
       "      <td> 12.12</td>\n",
       "      <td> 10.05</td>\n",
       "      <td> 17.61</td>\n",
       "      <td> 19.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-08</th>\n",
       "      <td> 16.96</td>\n",
       "      <td> 14.05</td>\n",
       "      <td> 14.34</td>\n",
       "      <td> 12.06</td>\n",
       "      <td> 11.21</td>\n",
       "      <td> 26.19</td>\n",
       "      <td> 19.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-15</th>\n",
       "      <td> 11.51</td>\n",
       "      <td> 13.28</td>\n",
       "      <td> 14.06</td>\n",
       "      <td> 13.35</td>\n",
       "      <td> 12.73</td>\n",
       "      <td> 21.20</td>\n",
       "      <td> 30.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30</th>\n",
       "      <td> 15.16</td>\n",
       "      <td> 11.01</td>\n",
       "      <td> 13.56</td>\n",
       "      <td> 14.97</td>\n",
       "      <td>  9.25</td>\n",
       "      <td>  3.28</td>\n",
       "      <td>  3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-06</th>\n",
       "      <td>  3.18</td>\n",
       "      <td>  3.34</td>\n",
       "      <td>  3.71</td>\n",
       "      <td>  4.48</td>\n",
       "      <td> 19.89</td>\n",
       "      <td> 28.44</td>\n",
       "      <td> 19.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-13</th>\n",
       "      <td> 15.78</td>\n",
       "      <td>  8.46</td>\n",
       "      <td> 10.83</td>\n",
       "      <td>  9.68</td>\n",
       "      <td>  7.81</td>\n",
       "      <td> 17.57</td>\n",
       "      <td> 12.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td> 11.86</td>\n",
       "      <td> 10.01</td>\n",
       "      <td>  9.44</td>\n",
       "      <td> 13.25</td>\n",
       "      <td>  7.71</td>\n",
       "      <td> 15.96</td>\n",
       "      <td> 14.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-27</th>\n",
       "      <td>  8.66</td>\n",
       "      <td> 11.29</td>\n",
       "      <td> 10.22</td>\n",
       "      <td> 10.78</td>\n",
       "      <td>  8.95</td>\n",
       "      <td> 24.02</td>\n",
       "      <td> 19.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-04</th>\n",
       "      <td>  9.89</td>\n",
       "      <td> 16.26</td>\n",
       "      <td> 20.37</td>\n",
       "      <td> 17.30</td>\n",
       "      <td> 27.24</td>\n",
       "      <td>  2.78</td>\n",
       "      <td> 13.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-11</th>\n",
       "      <td> 20.44</td>\n",
       "      <td> 26.25</td>\n",
       "      <td> 10.03</td>\n",
       "      <td>  9.49</td>\n",
       "      <td>  7.10</td>\n",
       "      <td> 22.12</td>\n",
       "      <td> 38.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-18</th>\n",
       "      <td> 19.41</td>\n",
       "      <td> 26.21</td>\n",
       "      <td> 16.92</td>\n",
       "      <td> 11.93</td>\n",
       "      <td> 11.95</td>\n",
       "      <td> 14.26</td>\n",
       "      <td> 16.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-25</th>\n",
       "      <td> 19.15</td>\n",
       "      <td> 21.42</td>\n",
       "      <td> 26.31</td>\n",
       "      <td> 23.40</td>\n",
       "      <td> 25.15</td>\n",
       "      <td> 31.09</td>\n",
       "      <td> 38.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td> 19.58</td>\n",
       "      <td> 16.82</td>\n",
       "      <td>  7.20</td>\n",
       "      <td>  7.04</td>\n",
       "      <td>  6.09</td>\n",
       "      <td> 22.37</td>\n",
       "      <td> 24.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td> 17.30</td>\n",
       "      <td> 24.52</td>\n",
       "      <td> 21.51</td>\n",
       "      <td> 20.48</td>\n",
       "      <td> 24.84</td>\n",
       "      <td> 52.09</td>\n",
       "      <td> 38.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td> 36.11</td>\n",
       "      <td> 40.80</td>\n",
       "      <td> 34.53</td>\n",
       "      <td> 21.67</td>\n",
       "      <td> 34.53</td>\n",
       "      <td> 36.26</td>\n",
       "      <td> 43.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-22</th>\n",
       "      <td> 26.73</td>\n",
       "      <td> 34.62</td>\n",
       "      <td> 29.39</td>\n",
       "      <td> 20.32</td>\n",
       "      <td> 20.88</td>\n",
       "      <td> 38.75</td>\n",
       "      <td> 11.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>  6.44</td>\n",
       "      <td> 20.88</td>\n",
       "      <td> 25.07</td>\n",
       "      <td> 16.69</td>\n",
       "      <td> 21.49</td>\n",
       "      <td> 25.04</td>\n",
       "      <td> 34.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-06</th>\n",
       "      <td> 20.65</td>\n",
       "      <td> 31.67</td>\n",
       "      <td> 25.35</td>\n",
       "      <td> 22.24</td>\n",
       "      <td> 27.42</td>\n",
       "      <td> 25.53</td>\n",
       "      <td> 28.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td> 28.27</td>\n",
       "      <td> 26.87</td>\n",
       "      <td> 23.20</td>\n",
       "      <td> 16.24</td>\n",
       "      <td> 16.70</td>\n",
       "      <td> 32.11</td>\n",
       "      <td> 46.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td> 21.05</td>\n",
       "      <td> 21.71</td>\n",
       "      <td> 33.52</td>\n",
       "      <td> 23.83</td>\n",
       "      <td> 11.86</td>\n",
       "      <td>  2.63</td>\n",
       "      <td>  9.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-27</th>\n",
       "      <td> 28.18</td>\n",
       "      <td> 31.55</td>\n",
       "      <td> 24.98</td>\n",
       "      <td> 23.89</td>\n",
       "      <td> 21.76</td>\n",
       "      <td> 36.43</td>\n",
       "      <td> 29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-03</th>\n",
       "      <td> 28.09</td>\n",
       "      <td> 26.67</td>\n",
       "      <td> 27.82</td>\n",
       "      <td> 20.76</td>\n",
       "      <td> 21.25</td>\n",
       "      <td> 18.64</td>\n",
       "      <td> 22.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-10</th>\n",
       "      <td> 15.84</td>\n",
       "      <td> 27.90</td>\n",
       "      <td> 18.15</td>\n",
       "      <td> 14.18</td>\n",
       "      <td> 20.34</td>\n",
       "      <td> 14.20</td>\n",
       "      <td> 11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-17</th>\n",
       "      <td> 35.46</td>\n",
       "      <td> 20.40</td>\n",
       "      <td> 25.53</td>\n",
       "      <td> 19.69</td>\n",
       "      <td> 19.16</td>\n",
       "      <td> 17.94</td>\n",
       "      <td> 30.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-24</th>\n",
       "      <td> 24.80</td>\n",
       "      <td> 25.70</td>\n",
       "      <td> 19.50</td>\n",
       "      <td>  8.75</td>\n",
       "      <td> 11.14</td>\n",
       "      <td> 20.14</td>\n",
       "      <td> 25.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td> 21.44</td>\n",
       "      <td> 23.77</td>\n",
       "      <td> 31.41</td>\n",
       "      <td> 22.02</td>\n",
       "      <td> 32.24</td>\n",
       "      <td>  7.86</td>\n",
       "      <td>  3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-07</th>\n",
       "      <td> 10.06</td>\n",
       "      <td> 46.16</td>\n",
       "      <td> 29.70</td>\n",
       "      <td> 18.36</td>\n",
       "      <td> 25.38</td>\n",
       "      <td> 27.38</td>\n",
       "      <td> 19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-14</th>\n",
       "      <td> 13.69</td>\n",
       "      <td>  8.32</td>\n",
       "      <td> 16.37</td>\n",
       "      <td> 20.40</td>\n",
       "      <td> 14.15</td>\n",
       "      <td> 26.00</td>\n",
       "      <td> 22.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-21</th>\n",
       "      <td> 15.01</td>\n",
       "      <td>  9.21</td>\n",
       "      <td> 20.39</td>\n",
       "      <td> 13.15</td>\n",
       "      <td> 15.69</td>\n",
       "      <td> 19.85</td>\n",
       "      <td> 24.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-28</th>\n",
       "      <td> 14.39</td>\n",
       "      <td> 13.64</td>\n",
       "      <td> 29.95</td>\n",
       "      <td>  6.83</td>\n",
       "      <td> 10.37</td>\n",
       "      <td> 18.12</td>\n",
       "      <td> 25.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-05</th>\n",
       "      <td> 11.32</td>\n",
       "      <td> 11.64</td>\n",
       "      <td> 31.27</td>\n",
       "      <td>  9.56</td>\n",
       "      <td> 12.28</td>\n",
       "      <td> 21.43</td>\n",
       "      <td> 16.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-12</th>\n",
       "      <td>  9.62</td>\n",
       "      <td> 10.63</td>\n",
       "      <td>  7.41</td>\n",
       "      <td> 11.37</td>\n",
       "      <td>  8.34</td>\n",
       "      <td>  5.62</td>\n",
       "      <td>  3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-19</th>\n",
       "      <td>  2.69</td>\n",
       "      <td>  3.40</td>\n",
       "      <td>  3.00</td>\n",
       "      <td>  3.80</td>\n",
       "      <td>  6.64</td>\n",
       "      <td> 13.83</td>\n",
       "      <td> 22.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mon   Tues    Wed  Thurs    Fri    Sat    Sun\n",
       "timestamp                                                  \n",
       "2014-05-26  12.46  25.68  17.00   7.48   8.24   9.50   8.28\n",
       "2014-06-02  12.09  16.34  16.03  12.65  12.88  34.12  22.44\n",
       "2014-06-09  33.98  16.52  24.09  10.20  33.92  16.81  22.76\n",
       "2014-06-16  15.07  22.25  26.37  25.47  10.69  26.03  29.15\n",
       "2014-06-23  20.96  26.21  18.13  22.89  19.40  32.24  28.22\n",
       "2014-06-30  20.47  19.01  30.58  19.25  33.93  17.85  30.53\n",
       "2014-07-07  19.01  22.15  21.66  20.65  15.48  31.25  40.61\n",
       "2014-07-14  22.37  20.21  19.83  16.44  16.78  29.20  18.26\n",
       "2014-07-21  14.89  19.33  21.03  17.69  16.90  23.03  40.21\n",
       "2014-07-28  19.52  11.83   8.39  13.73  13.94  27.80  19.37\n",
       "2014-08-04  21.07  20.46  15.31  14.65  21.77  15.85  18.74\n",
       "2014-08-11  15.72  20.31  18.68   9.39  10.02  14.92  15.97\n",
       "2014-08-18  22.83  22.03  16.18  24.94  16.29  27.40  28.14\n",
       "2014-08-25  12.96  15.39  16.83  20.14   9.15  12.12   3.26\n",
       "2014-09-01  13.31  29.63  22.64  16.16  22.15  35.00  32.42\n",
       "2014-09-08  15.58  14.94  18.09  16.67  13.56   4.78   3.32\n",
       "2014-09-15  15.21  13.42  10.42   7.58   6.64   8.94  13.49\n",
       "2014-09-22   9.22  10.93   8.64   8.99   8.53   8.66   8.71\n",
       "2014-09-29  10.21   6.29   9.53   6.73  10.60   6.82  11.17\n",
       "2014-10-06   8.87   8.37   7.84   9.87   8.19  22.09   7.47\n",
       "2014-10-13  17.24   7.72   8.20  15.70   8.92  15.78  11.85\n",
       "2014-10-20   6.30  11.39   9.34  10.65  16.50   9.56  17.28\n",
       "2014-10-27  16.54   7.53   9.55   9.03   9.69  19.86  23.98\n",
       "2014-11-03  12.90  10.81   9.09  12.06   8.99  22.27  12.19\n",
       "2014-11-10  11.29  12.74  11.47  12.24  11.85  18.96  22.81\n",
       "2014-11-17  17.24  14.00  16.85  26.90  13.49  12.97  20.36\n",
       "2014-11-24   8.07   9.58  13.31  21.95  18.30  26.41  22.50\n",
       "2014-12-01   7.94  12.12  13.96  12.12  10.05  17.61  19.29\n",
       "2014-12-08  16.96  14.05  14.34  12.06  11.21  26.19  19.68\n",
       "2014-12-15  11.51  13.28  14.06  13.35  12.73  21.20  30.92\n",
       "...           ...    ...    ...    ...    ...    ...    ...\n",
       "2015-03-30  15.16  11.01  13.56  14.97   9.25   3.28   3.60\n",
       "2015-04-06   3.18   3.34   3.71   4.48  19.89  28.44  19.65\n",
       "2015-04-13  15.78   8.46  10.83   9.68   7.81  17.57  12.70\n",
       "2015-04-20  11.86  10.01   9.44  13.25   7.71  15.96  14.58\n",
       "2015-04-27   8.66  11.29  10.22  10.78   8.95  24.02  19.46\n",
       "2015-05-04   9.89  16.26  20.37  17.30  27.24   2.78  13.07\n",
       "2015-05-11  20.44  26.25  10.03   9.49   7.10  22.12  38.44\n",
       "2015-05-18  19.41  26.21  16.92  11.93  11.95  14.26  16.60\n",
       "2015-05-25  19.15  21.42  26.31  23.40  25.15  31.09  38.08\n",
       "2015-06-01  19.58  16.82   7.20   7.04   6.09  22.37  24.49\n",
       "2015-06-08  17.30  24.52  21.51  20.48  24.84  52.09  38.18\n",
       "2015-06-15  36.11  40.80  34.53  21.67  34.53  36.26  43.26\n",
       "2015-06-22  26.73  34.62  29.39  20.32  20.88  38.75  11.74\n",
       "2015-06-29   6.44  20.88  25.07  16.69  21.49  25.04  34.28\n",
       "2015-07-06  20.65  31.67  25.35  22.24  27.42  25.53  28.23\n",
       "2015-07-13  28.27  26.87  23.20  16.24  16.70  32.11  46.26\n",
       "2015-07-20  21.05  21.71  33.52  23.83  11.86   2.63   9.74\n",
       "2015-07-27  28.18  31.55  24.98  23.89  21.76  36.43  29.80\n",
       "2015-08-03  28.09  26.67  27.82  20.76  21.25  18.64  22.03\n",
       "2015-08-10  15.84  27.90  18.15  14.18  20.34  14.20  11.77\n",
       "2015-08-17  35.46  20.40  25.53  19.69  19.16  17.94  30.38\n",
       "2015-08-24  24.80  25.70  19.50   8.75  11.14  20.14  25.06\n",
       "2015-08-31  21.44  23.77  31.41  22.02  32.24   7.86   3.24\n",
       "2015-09-07  10.06  46.16  29.70  18.36  25.38  27.38  19.84\n",
       "2015-09-14  13.69   8.32  16.37  20.40  14.15  26.00  22.50\n",
       "2015-09-21  15.01   9.21  20.39  13.15  15.69  19.85  24.67\n",
       "2015-09-28  14.39  13.64  29.95   6.83  10.37  18.12  25.06\n",
       "2015-10-05  11.32  11.64  31.27   9.56  12.28  21.43  16.82\n",
       "2015-10-12   9.62  10.63   7.41  11.37   8.34   5.62   3.14\n",
       "2015-10-19   2.69   3.40   3.00   3.80   6.64  13.83  22.42\n",
       "\n",
       "[74 rows x 7 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~jtelszasz/387.embed\" height=\"525\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay at 0x108130150>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2015-02-23 00:00:00    0.26\n",
       "2015-02-23 01:00:00    0.62\n",
       "2015-02-23 02:00:00    0.26\n",
       "2015-02-23 03:00:00    0.27\n",
       "2015-02-23 04:00:00    0.64\n",
       "2015-02-23 05:00:00    0.61\n",
       "2015-02-23 06:00:00    2.43\n",
       "2015-02-23 07:00:00    1.57\n",
       "2015-02-23 08:00:00    0.70\n",
       "2015-02-23 09:00:00    0.33\n",
       "2015-02-23 10:00:00    0.34\n",
       "2015-02-23 11:00:00    0.69\n",
       "2015-02-23 12:00:00    0.36\n",
       "2015-02-23 13:00:00    0.34\n",
       "2015-02-23 14:00:00    0.36\n",
       "...\n",
       "2015-02-28 09:00:00    0.86\n",
       "2015-02-28 10:00:00    0.54\n",
       "2015-02-28 11:00:00    0.48\n",
       "2015-02-28 12:00:00    2.38\n",
       "2015-02-28 13:00:00    1.86\n",
       "2015-02-28 14:00:00    0.85\n",
       "2015-02-28 15:00:00    3.81\n",
       "2015-02-28 16:00:00    1.87\n",
       "2015-02-28 17:00:00    3.45\n",
       "2015-02-28 18:00:00    1.61\n",
       "2015-02-28 19:00:00    4.21\n",
       "2015-02-28 20:00:00    0.55\n",
       "2015-02-28 21:00:00    0.77\n",
       "2015-02-28 22:00:00    0.32\n",
       "2015-02-28 23:00:00    0.30\n",
       "Name: USAGE, Length: 144"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_apt['USAGE'].ix[new_apt['USAGE'].index.week == new_apt['USAGE'].index[-1].week]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Last Week Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values does not match length of index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-f371f76964e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m24\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mlast_week_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_week\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlast_week\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhour\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mlast_week_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweek_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2108\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2109\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2110\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2186\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2187\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2188\u001b[0m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2258\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2259\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseries\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_sanitize_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2260\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sanitize_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2261\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2262\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Justin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_sanitize_index\u001b[0;34m(data, index, copy)\u001b[0m\n\u001b[1;32m   2543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2544\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2545\u001b[0;31m         raise ValueError('Length of values does not match length of '\n\u001b[0m\u001b[1;32m   2546\u001b[0m                          'index')\n\u001b[1;32m   2547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length of values does not match length of index"
     ]
    }
   ],
   "source": [
    "last_week = pd.DataFrame(new_apt['USAGE'].ix[new_apt['USAGE'].index.week == new_apt['USAGE'].index[-1].week])\n",
    "#last_week.plot()\n",
    "\n",
    "# Change this if not a full week at end of month!!\n",
    "week_index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat']\n",
    "\n",
    "# Change size of dataframe if not a full week at end of month!!\n",
    "last_week_array = pd.DataFrame(np.zeros([6,24]))\n",
    "\n",
    "for i in range(24):\n",
    "    last_week_array[i] = np.array(last_week[last_week.index.hour==i])\n",
    "    \n",
    "last_week_array.index = week_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XTJ1yBpOnHF3zdUuWL6j5mu4aNmhUbm0l+eaNi3N8LZPufU6dfOKFHHf8ITVf969XD+lW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\nZmZmZtaQnOyYmZmZmVlD+n/hfbZDqVHZigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e2b690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#sundays = pd.Series(array[array.index.dayofweek==0].index)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[10,25])\n",
    "\n",
    "title('Week of %s\\n'%last_week.ix[0].name.strftime('%B %d, %Y'), fontsize=18)\n",
    "plt.imshow(last_week_array, interpolation='none', cmap='YlGnBu')\n",
    "colorbar_ax = fig.add_axes([1.05, 0.45, .04, .1])\n",
    "cb = plt.colorbar(cax = colorbar_ax)\n",
    "#plt.tick_params(axis=\"both\", which=\"both\", bottom=\"on\", top=\"on\",\n",
    "#                labelbottom=\"on\", labeltop='on', left=\"on\", right=\"off\", labelleft=\"on\") \n",
    "\n",
    "cb.set_label('kilowatt-hours',fontsize = 16)\n",
    "\n",
    "#xticks = range(0,24,4)\n",
    "#yticks = range(0,len(array),7)\n",
    "\n",
    "#ax.set_xticks(xticks)\n",
    "ax.set_yticks(range(7))\n",
    "ax.set_yticklabels(week_index, fontsize=10)\n",
    "#ax.yaxis_date()\n",
    "\n",
    "#ax.yaxis.set_major_formatter(mdates.DateFormatter('%Y')) This didn't work for me\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~jtelszasz/244.embed\" height=\"525\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay at 0x109e2b190>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = Data([\n",
    "    Heatmap(\n",
    "        x = range(0,24,1),\n",
    "        y = last_week_array.index,\n",
    "        z = np.array(last_week_array),\n",
    "        colorscale='YIGnBu',\n",
    "        reversescale=True)\n",
    "])\n",
    "\n",
    "layout = Layout(\n",
    "    title='Week of %s <br>'%last_week.ix[0].name.strftime('%B %d, %Y'),\n",
    "    yaxis = YAxis(autorange='reversed')\n",
    ")\n",
    "#plot_url = py.plot(data, filename='Last Week Heat Map')\n",
    "fig = Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Last Week Heat Map')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average Week Hourly TS and Heat Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "groupby_day_hour = new_apt['USAGE'].groupby([new_apt.index.dayofweek,new_apt.index.hour])\n",
    "avg_week_array = pd.DataFrame(np.zeros([6,24]))\n",
    "for i in range(6):\n",
    "    for j in range(24):\n",
    "        avg_week_array[j].ix[i] = groupby_day_hour.get_group((i,j)).mean()\n",
    "avg_week_array.index = week_index\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ub2PD+q5dWZyZyR2xsYx0ceHt9u1pYV7DTnq9e8MLL8Cdd8KOHfRr24+X+r/E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\nwH4viH/zG0F7O7S1GYtpOkvntqpuLZa3JZ772rbvhiFRVdtJJLYUy0a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WLDFKTQ2MHw9T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\nSCQSiUQyoJGCWCKRSCQSiUQyoPn/3+jH93u27OkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6cb310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,3]) \n",
    "ax1 = plt.subplot(1,1,1)\n",
    "\n",
    "plot1 = plot(avg_week_array.transpose())\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aA9gVuGAasUiSJEkbjLGOt1l2OM1Ka8va4++2x73bb2lWfPvPqVQ8nQUK/req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\nkiRJkkbS/wfEAmy/U9cGJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6ef2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sundays = pd.Series(array[array.index.dayofweek==0].index)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[10,25])\n",
    "\n",
    "title('Average Week\\n', fontsize=18)\n",
    "plt.imshow(avg_week_array, interpolation='none', cmap='YlGnBu')\n",
    "colorbar_ax = fig.add_axes([1.05, 0.45, .04, .1])\n",
    "cb = plt.colorbar(cax = colorbar_ax)\n",
    "#plt.tick_params(axis=\"both\", which=\"both\", bottom=\"on\", top=\"on\",\n",
    "#                labelbottom=\"on\", labeltop='on', left=\"on\", right=\"off\", labelleft=\"on\") \n",
    "\n",
    "cb.set_label('kilowatt-hours',fontsize = 16)\n",
    "\n",
    "#xticks = range(0,24,4)\n",
    "#yticks = range(0,len(array),7)\n",
    "\n",
    "#ax.set_xticks(xticks)\n",
    "ax.set_yticks(range(7))\n",
    "ax.set_yticklabels(avg_week_array.index, fontsize=10)\n",
    "#ax.yaxis_date()\n",
    "\n",
    "#ax.yaxis.set_major_formatter(mdates.DateFormatter('%Y')) This didn't work for me\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack these for Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# String citing the data source\n",
    "#url='http://donnees.ville.montreal.qc.ca/dataset/velos-comptage' \n",
    "#text_source = \"Source and info: <a href=\\\"{}\\\">\\\n",
    "#Données ouvertes de la Ville de Montréal</a>\".format(url)\n",
    "\n",
    "font_size = 16\n",
    "top_text = 'Week of %s\\n'%last_week.ix[0].name.strftime('%B %d, %Y')\n",
    "bottom_text = 'Average Week: %s through %s\\n'% (new_apt.ix[0].name.strftime('%B %d, %Y'),\n",
    "                                    new_apt.ix[len(new_apt)-1].name.strftime('%B %d, %Y')\n",
    "                                    )\n",
    "\n",
    "\n",
    "# Annotation generating function, \n",
    "#  to be used in all plots in this section\n",
    "def top_title(x=.5, y=1, text=top_text):\n",
    "    return Annotation(\n",
    "        text=text,          # annotation text\n",
    "        showarrow=False,    # remove arrow \n",
    "        xref='paper',     # use paper coords\n",
    "        yref='paper',     #  for both coordinates\n",
    "        xanchor='center',  # x-coord line up with right end of text \n",
    "        yanchor='bottom', # y-coord line up with bottom end of text \n",
    "        x=x,              # position's x-coord\n",
    "        y=y,               #   and y-coord\n",
    "        font = Font(size = font_size)\n",
    "    )\n",
    "\n",
    "# Annotation generating function, \n",
    "#  to be used in all plots in this section\n",
    "def bottom_title(x=.5, y=.45, text=bottom_text):\n",
    "    return Annotation(\n",
    "        text=text,          # annotation text\n",
    "        showarrow=False,    # remove arrow \n",
    "        xref='paper',     # use paper coords\n",
    "        yref='paper',     #  for both coordinates\n",
    "        xanchor='center',  # x-coord line up with right end of text \n",
    "        yanchor='bottom', # y-coord line up with bottom end of text \n",
    "        x=x,              # position's x-coord\n",
    "        y=y,               #   and y-coord\n",
    "        font = Font(size = font_size)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~jtelszasz/245.embed\" height=\"525\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay at 0x107735610>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height = 600\n",
    "width = 800\n",
    "\n",
    "zmin = 0\n",
    "zmax = 5\n",
    "\n",
    "heatmap_avg = Heatmap(\n",
    "    x = range(0,24,1),\n",
    "    y = avg_week_array.index,\n",
    "    z = np.array(avg_week_array),\n",
    "    colorscale='YIOrRd',\n",
    "    reversescale=True,\n",
    "    zauto=False,\n",
    "    zmin = zmin,\n",
    "    zmax = zmax\n",
    ")\n",
    "\n",
    "heatmap_last_week = Heatmap(\n",
    "    x = range(0,24,1),\n",
    "    y = last_week_array.index,\n",
    "    z = np.array(last_week_array),\n",
    "    colorscale='YIOrRd',\n",
    "    yaxis = 'y2',\n",
    "    reversescale=True,\n",
    "    zauto=False,\n",
    "    zmin = zmin,\n",
    "    zmax = zmax,\n",
    "    colorbar = ColorBar(\n",
    "        title = 'kWh',\n",
    "        titleside='right'\n",
    "        )\n",
    ")\n",
    "\n",
    "data = Data([heatmap_avg, heatmap_last_week])\n",
    "\n",
    "layout = Layout(  \n",
    "    autosize = False,\n",
    "    height = height,\n",
    "    width = width,\n",
    "    yaxis = YAxis(domain=[0, .45], \n",
    "                  autorange='reversed'\n",
    "                  ),\n",
    "    yaxis2 = YAxis(domain=[.55, 1], \n",
    "                   autorange='reversed'\n",
    "                   ),\n",
    "    annotations = Annotations([top_title(),bottom_title()])\n",
    ")\n",
    "\n",
    "fig = Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Avg Week Last Week Heatmap Comparison')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line plot - each line is hourly plot of kWh for one day. <br> Most recent days and the average day highlighted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define a function to calculate average weekday and weekend\n",
    "\n",
    "def avg_day(df,start_date,end_date):\n",
    "    # df is pandas df, has 'USAGE' and 'tempF' fields, indexed by pandas datetime\n",
    "    # date_range is string for .ix call\n",
    "    # returns average_day\n",
    "    # average_day[True\n",
    "    \n",
    "    df_dates = df.ix[start_date:end_date]\n",
    "    \n",
    "    # First groupby weekday/weekend and hour of day\n",
    "    weekday_weekend_hour = df_dates.groupby([(df_dates.index.dayofweek==5)|(df_dates.index.dayofweek==6),df_dates.index.hour])\n",
    "\n",
    "    # Calculate an average weekday and average weekend by hour (electricity and outdoor temp)\n",
    "    average_days = pd.DataFrame(weekday_weekend_hour['USAGE'].mean()) #,weekday_weekend_hour['tempF'].mean()])\n",
    "    \n",
    "    average_weekend = average_days.ix[True]\n",
    "    average_weekday = average_days.ix[False]\n",
    "\n",
    "    return average_weekday, average_weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weekday_newapt, weekend_newapt = avg_day(new_apt,'24-may-2014','15-feb-2015')\n",
    "average_day = new_apt['USAGE'].groupby(new_apt.index.hour).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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diAwFfoWbe64tblTMyIOoqqbWrY61ssDOGGOMMcYYsy9LrI9dta1FTgVewQVz\n3wNf4aYniGTBlzHGGGOMMcY0orhb7ETk78ARwGmq+kaD1io2CxqNMcYYY0yLIGJjAZr6iRGr1a/F\nDhfUPd+EQZ0xxhhjjDEtSlOPZ2FarkQfDET2katJEbAlob0bY4wxxhhjjGlwiQR2fwMGNFRFjDHG\nGGOMMcbUTSKB3Q3AgSJyi1jCsDHGGGOMMWYvNXToUB5//PG4yt5+++1MmDChgWtUu5iBnYg8KSJP\n+C/gVmAVbs65b0RkXvj6iLLGGGOMMWYfoapUVkYbLN00Z7169SInJ4fWrVvTqVMnJkyYwM6dO5us\nPkuWLKF79+4x17///vvk5uZW6bc4efLkqMsuv/zyetVFROLu49Zc2rxqarG7ALgw4jXUW9cLOD3K\nev9ljDHGGGP2EYFAgLKysqauhkmQiLBgwQJ27drF8uXLWbFiBXfffXdTVyum4447jmAwyMcffxxa\ntnTpUrp3715t2ZAhQ5qiik2qpsCudz1exhhjjDFmHxEIBAgGgwSDwaauiqmjjh07Mnz4cFatWhVa\n9sEHH3DCCSfQtm1b+vbtyzvvvBNat3XrVi666CK6du1KXl4eY8aMCa1bsGABffv2pW3btgwcOJAV\nK1aE1vXq1YsZM2Zw1FFH0aZNG8455xzKysooKipixIgRrFu3jtatW5Obm8uPP/5YpY7p6en079+f\nwsJCADZu3EhFRQXjxo2rsuyrr74iPz8fVeXee+/loIMOon379px99tls27YtrvMLt379eo488khm\nzJgBwOrVqxkyZAi5ubkMHz6czZs3Vyl/1lln0blzZ9q0acOQIUP44osvAPj73/9Op06dqrQuzps3\nj759+8bxCdUuZmCnqt/W9ZWUmhljjDHGmBYhEAiQkZFBIBBo6qqYBPlBxg8//MCiRYv42c9+BsDa\ntWsZOXIkt956K9u2bWP69OmMHTuWLVvcIPkTJkygtLSUL774go0bN3LttdcC8OmnnzJp0iRmzpzJ\n1q1bmTJlCqNHj6aiogJwrYRz587l9ddfZ/Xq1Xz++ec89dRTtGrVikWLFtGlSxd27drFzp076dSp\nU7X65ufnh4K4wsJCBg0axMCBA6ss6927N126dOHBBx9k/vz5FBYWsn79etq2bcuVV14Z1/n5Vq9e\nzdChQ7n66qu57rrrABg/fjz9+vVjy5Yt3HLLLTz99NNV0jFPPfVU/vWvf7Fp0yaOOeYYzjvvPAD6\n9etHu3b8PTw2AAAgAElEQVTteP3110NlZ82axQUXXFCfj3APVY35AgYBqTWVaeSXMcYYY4xpZnbv\n3q2BQECLi4ubuirNirvVbr569uyp++23n7Zu3VpFRE8//XQNBAKqqnrvvffqhAkTqpQ/+eST9emn\nn9Z169ZpSkqKbt++vdo+L7vsMr3llluqLDvkkEO0sLBQVVV79eqls2fPDq379a9/rZdddpmqqi5e\nvFi7detWY50XL16s7dq1U1XVq6++Wh977DHdvXu3duzYMbTs4osvVlXVQw89VN96663QtuvWrdP0\n9HStrKys8fxUVYcOHarXXnut9urVS5977rlQmTVr1mhaWlqVa338+PF6/vnnR63vtm3bVER0586d\nqure1/POO09VVbds2aI5OTn6448/Rt22husnaqxU2wTlhcBuEVkKvAW8parLkxNSGmOMMcaYli4Y\nDCIipKSkWCpmHfzyl79Myn6mT5+e8DYiwiuvvMKwYcMoLCxk1KhRfPTRRxx//PGsWbOGuXPn8uqr\nr4bKV1ZWMmzYML7//nvy8vLYf//9q+1zzZo1FBQU8NBDD4WWVVRUsG7dutDv4S1x2dnZVdbVpn//\n/uzevZuVK1eydOlSrrzySlq1akX37t1ZuXIlhYWFTJs2LVSXMWPGkJKyJ0kxLS2NDRs21Hh+4Bq/\nZs+eTZ8+fRg7dmyozLp162jbti3Z2dmhZT179uT7778HXOv1zTffzIsvvsimTZtISUlBRNi8eTOt\nW7fmvPPO4/DDD6e4uJgXXniB/Px8OnbsGPf516S2wG4p0B8Y4b0Qkc3AYlyg9zdV/XdSamKMMcYY\nY1qcYDBIamoqAKmpqQQCgdDvpnZ1CcgaQn5+PldddRXXX389ixcvpkePHkyYMIFHH320Wtn169ez\ndetWduzYUS2469GjBzfffDM33XRTwnWIZ3TJrKws+vXrx/z581m/fj0HH3wwAIMHD2b+/PmsWLGC\n/Pz8UF2efPJJBgyoPhV3Tefn1+WOO+5g4cKFjB8/nueee46UlBQ6d+7Mtm3bKC4uJicnB3ABpH/N\nP/vss8yfP5+33nqLnj17sn37dvLy8kIpr926daN///7MmzePZ555hiuuuCLh9ymWGuexU9UhQFvg\nF8D9wMdAHnAW8AjwtYisFpHHRORcEUlOuGmMMcYYY1qE8EAuNTXVpj1owaZNm8ayZcv48MMPOf/8\n83n11Vd54403CAQClJaWsmTJEtauXUvnzp0ZMWIEV1xxBdu3b6eioiLUx23y5Mk88sgjLFu2DFWl\nqKiI1157jd27d9d6/I4dO7Jly5Zap1zIz8/ngQceYODAgaFlgwYN4oEHHqBLly785Cc/AeCyyy7j\npptu4rvvvgNg06ZNzJ8/H6DG8/Olp6czd+5cioqKmDhxIqpKz549Oe6447jtttuoqKjg3XffZcGC\nBaFtdu/eTWZmJnl5eRQVFUUNcCdOnMh9993HypUrOeOMM2p9X+JV6wTlqlqsqm+o6g2q2g9oB4wB\nHgL+AfQELgZmA+tEZEXsvRljjDHGmL1JeGCXlpZmA6i0YO3bt+eCCy7gvvvuo1u3brzyyivcc889\ndOjQgR49ejBjxoxQuu2sWbNIT0/n0EMPpWPHjjz44IMAHHvsscycOZOpU6eSl5dHnz59KCgoiNka\nFz5f3KGHHsq5555L7969ycvLqzYqpm/IkCFs3ryZQYMGhZYNHDiQTZs2MXjw4NCya665htGjRzN8\n+HByc3MZMGAAy5YtA4h5fn7Lmi89PZ158+axYcMGJk2ahKry7LPP8uGHH5KXl8edd95ZZfCTiRMn\n0rNnT7p27coRRxzBgAEDqp37GWecwXfffceYMWPIysqK67OJh0RWPuEdiBwAnAz8CvgprpNfQ7W/\n16+yxhhjjDEmqYqKimjVqlWV33NycprNpM1NSUSqBQrGAPTp04e//OUvoT590dRw/UT9ctXWx66m\nAx0J/CcwDMgHWnur4u/9aIwxxhhjWqxgMFhlYArY02qXllbn20xj9mrz5s1DRGoM6uoi7m+ciBzM\nnkBuKC4lE+BH4DXcgCqLVfVfiVRARL4FekRZ9VdVHZnIvowxxhhjTOOJNlBKWloaFRUVFtgZE8XQ\noUP58ssvmTVrVtL3XWMqpohciAvkfg509RZvApZ4r8Wq+mW9KiDSDgj/H6ELbpCWC1U18oytLdsY\nY4wxppkoLS0lPT29WnAXmZ65r7JUTFMfyU7FfAIXTP0VuA8XyK2qVw0jqGqV6d1FZDKwA3ghmccx\nxhhjjDHJFS0VE9wNaax1xpiGEU8bueBa7TKAViLSCvhIVZM+A6W4XraTgGdUtSzZ+zfGGGOMMcmj\nqlEHSUlLS6OyspKMjIwmqJUx+6baAruu7OlXNww4yVu+U0SWAm/jWvE+S1J9TgJ6ATOTtD9jjDHG\nGNMAAoFAzBa5tLQ0ysrsGb0xjSmh6Q5E5ED2BHk/Bzp4q7YC77An0PuiTpURmQt0V9X+MYpYkrIx\nxhhjTDNQUVGBqsZslbN+dtbHztRPon3s6jWPnYgcwZ5ALx9oQx3nsRORDsD3wBWq+niMYvbNMMaY\nZq6ystJGwzNmHxBr4JR41+8LLLAz9ZFoYFffHq1bw167azpQHC4ESoE59ayTMcaYJqKqFBcX242M\nMfuAmlIxAVJTU6msrGzEGhmTPEOHDuXxx2O1NSVPr169eOutt5Kyr4QCOxHJE5GxIvKwiPwD18JW\ngAvKugGrgAcTrYQ3aMolwHOqWpzo9sYYY5oH/0bPbuaM2bv5D2+iDZzi8ycqN81Xr169yMnJoXXr\n1nTq1IkJEyawc+fOJqvPkiVL6N69e8z177//Prm5uVUeHk6ePDnqsssvv7xedRGRGq/vZEnmcWoM\n7ERkPxE5RUSmi8gnwEZgLnA5cAiwBngcGA90UtWfquq0OtRjKHAgNmiKMca0aP4oeHYzZ8zeLRgM\n1ppi6d+sWgt+8yUiLFiwgF27drF8+XJWrFjB3Xff3dTVium4444jGAzy8ccfh5YtXbqU7t27V1s2\nZMiQpqhik6qtxW4rsAC4FugLbMalSl4C9FbV3qo6WVWfU9WNda2Eqi5W1VRV/aiu+zDGGNP0AoEA\n6enpFtgZs5cLBAJx9Z1LTU21/w9aiI4dOzJ8+HBWrdozZfUHH3zACSecQNu2benbty/vvPNOaN3W\nrVu56KKL6Nq1K3l5eYwZMya0bsGCBfTt25e2bdsycOBAVqxYEVrXq1cvZsyYwVFHHUWbNm0455xz\nKCsro6ioiBEjRrBu3Tpat25Nbm4uP/74Y5U6pqen079/fwoLCwHYuHEjFRUVjBs3rsqyr776ivz8\nfFSVe++9l4MOOoj27dtz9tlns23btrjOL9z69es58sgjmTFjRq3bDR06lFtvvZVBgwaRm5vLySef\nzJYte6btnjVrFj179qR9+/bcc8898X9AcagtsCsGXgGuAY5Q1U6qep6qPqGq3ya1JsYYY1q0YDAY\nSinxJyc2xuydautf5/PnszPNl9+i+sMPP7Bo0SJ+9rOfAbB27VpGjhzJrbfeyrZt25g+fTpjx44N\nBSkTJkygtLSUL774go0bN3LttdcC8OmnnzJp0iRmzpzJ1q1bmTJlCqNHj6aiogJwrYRz587l9ddf\nZ/Xq1Xz++ec89dRTtGrVikWLFtGlSxd27drFzp076dSpU7X65ufnh4K4wsJCBg0axMCBA6ss6927\nN126dOHBBx9k/vz5FBYWsn79etq2bcuVV14Z1/n5Vq9ezdChQ7n66qu57rrr4tpuzpw5PPXUU2zc\nuJHy8nKmT58OwBdffMEVV1zB7NmzWbduHVu2bOGHH35IzgdJ7YFdO1Udo6oPxTuFgTRGMqoxxphm\nJ3w0TOtbY8zeLZ5UTLAWu+ZOVTn99NPJzc2lR48eHHjggfzmN78B4JlnnuGUU07hF7/4BQAnnngi\nxx13HK+99hrr169n0aJFPPLII+y///6kpaUxePBgAB599FGmTJlCv379EBEmTpxIZmYmH3zwQei4\nV199NZ06daJt27aMGjWKzz77LFSf2uTn5/Puu+8CLuUyPz+fAQMGhPYfnob5yCOPcPfdd9OlSxfS\n09O57bbbePHFFwkEAjWen2/VqlUMGzaMO++8k0suuaTW9wVc4HrRRRdx0EEHkZWVxbhx40Ln9+KL\nLzJq1CgGDRpERkYGd911V1wPSOJV43jUqhr6JorIeao6O459Pg5cXN+KGWOMaVkCgQCZmZmAu5kr\nLy8nPT29iWtljEk2VU1osAe/BT+ZN7B7k9dff50333yz2vKTTjqJk08+Oa7yscrWRkR45ZVXGDZs\nGIWFhYwaNYqPPvqI448/njVr1jB37lxeffXVUPnKykqGDRvG999/T15eHvvvv3+1fa5Zs4aCggIe\neuih0LKKigrWrVsX+j28JS47O7vKutr079+f3bt3s3LlSpYuXcqVV15Jq1at6N69OytXrqSwsJBp\n06aF6jJmzJgq115aWhobNmyo8fzAXeezZ8+mT58+jB07tsr51bRdtPPbvdtNHrBu3Tq6desWWpeT\nk0O7du3iPvfaJDLR0BMisl5V345VQEQewY2QaYGdMcbsY8Jv3FJTUy0V05i9VLxpmD4/HTPWROb7\nupNPPjmhoCzR8vHKz8/nqquu4vrrr2fx4sX06NGDCRMm8Oijj1Yru379erZu3cqOHTuqBXc9evTg\n5ptv5qabbkq4DvE8MMjKyqJfv37Mnz+f9evXc/DBBwMwePBg5s+fz4oVK8jPzw/V5cknn2TAgAHV\n9lPT+fl1ueOOO1i4cCHjx4/nueeeIyUlpdbtatKlSxf+8Y9/hH4vLi6ulvpZH4k8OvkXME9Efhpt\npYj8D3Ap8FIyKmaMMabliDaQQkpKigV3xuyF4k3D9Flqdssxbdo0li1bxocffsj555/Pq6++yhtv\nvEEgEKC0tJQlS5awdu1aOnfuzIgRI7jiiivYvn07FRUVoT5ukydP5pFHHmHZsmWoKkVFRbz22muh\nVquadOzYkS1bttQ65UJ+fj4PPPAAAwcODC0bNGgQDzzwAF26dOEnP/kJAJdddhk33XQT3333HQCb\nNm1i/vz5ADWeny89PZ25c+dSVFTExIkTUdW4touVUjp27FgWLFjAe++9R3l5ObfeemtS/04mEtiN\nAIqAhSLSLXyFiNwHXA38FTgnabUzxhjTIlRWVla70bPJiY3ZO8U7IqbPHvK0HO3bt+eCCy7gvvvu\no1u3brzyyivcc889dOjQgR49ejBjxozQZzlr1izS09M59NBD6dixIw8+6KayPvbYY5k5cyZTp04l\nLy+PPn36UFBQELM1Lnwet0MPPZRzzz2X3r17k5eXV21UTN+QIUPYvHkzgwYNCi0bOHAgmzZtCvX1\nA7jmmmsYPXo0w4cPJzc3lwEDBrBs2TKAmOcXGZSlp6czb948NmzYwKRJk+jatWut24Wfa/j5HX74\n4Tz88MOMHz+eLl26kJeXV+O8fYmSROYWEZGjgELcxOQDVXWHiNwB3AK8BYxU1bKk1a46mwjFGGOa\noeLiYrKzs6v8MQsGg5SVlZGdnd2ENTPGJFtRURGtWrVKaJvS0lLS09MTCgj3BiJi8/iZOqvh+oka\nJSfSxw5VXS4iY3Etc/NF5G1cULcUGN3AQZ0xxphmSFWjDqZgT+mjU1UCgUBoBFFjWpJEB07x+f3s\n9rXAzpjGlPDwRKr6N9zgKIOB24APgVNVtSTJdTPGGNMC1BSkpKSkWN+aCH6fDGNaokTTMH027YEx\nDS/m40IRya9hu++Bl4F84LfAMeFPb1S1MFkVNMYY07yFz18XyR80wZ7S7+H3O7Th30191LXlrL7q\n+n3269pU9TZmXxCzj52I1DV/RlW1of6CW5KyMcY0M0VFReTk5ES9WQsGg5SWlpKTk9MENWueioqK\nQvP72fDvpq5KSkpITU1t9GuouLiYrKysOj2UKCsrIzU1dZ9KQ7Y+dqY+ktnH7s461sGuXmOM2UcE\ng8EqI35FSklJsZuaMP77lZaWRmlpqQV2ps6CwSCq2ujXkKrWuaU5LS2NioqKfSqwM6Yxxfxmqert\njVgPY4wxLVA8g4D4fWssHXNP2qof8FpamqkL/wFBY19D/nHrKjU11fqXGtOALLnfGGNMndXUv85n\n89ntEf5+2WASpq78CcL9kSYb+7j1ISI2Wq4xDcQCO2OMMXUWzwAg/gAqpmoam70vpq78aQPS09Mb\nNbBLRst7YwejxuxLYv41FpGFInJ8XXcsIseLyGt13d4YY0zzFggE4uprE54yti+LvCm2lkxTV/61\n5M8V2VjfrWQFdvZAw5iGUdNf5AOBD0RksYhcLCK5te1MRHK9souBD4CD4qmEiHQWkadFZKOIlIjI\nqlqmWzDGGNPEEplk29IOq6et+oPO7OsBr0mMf734fd0aM1BKRn8+Pxg1zcvQoUPJy8ujvLy8qatS\nb7fffjvp6enk5uaSm5vLIYccwlVXXcWPP/7Y1FVrcDUFdocD07yfjwGbReQTEXlERH4jIleIyJUi\ncouI/EVEPgG2eGX/A7jK27ZGItIGeA83muYpwKHAVGBjfU7MGGNMw/LTweJhT+mjv1+WlmYSFdlq\n1ljXUDLnXbQHPc3Lt99+y7Jly+jQoQPz589P+v4b+/84EeHcc89l586dbNu2jZdeeokff/yRY489\ndq8P7mJ+Q1W1QlUfBLoDE4B3cYHapbipEP4IPATcAUwGDgPeAc4Deqjqw6oazyf5a2Ctql6oqh+p\n6hpVXayqX9bnxIwxxjQcP7Uy3hu9ff1GLta0EJaOaRIV2VLeWN+tZI5saw80mpeCggJOPPFEJkyY\nwNNPPw24OQfbtGnDqlWrQuU2bdpETk4OmzdvBmDBggX07duXtm3bMnDgQFasWBEq26tXL+6//36O\nPPJIWrduTSAQ4N577+Wggw4iNzeXww8/nJdffjlUPhgMct1113HAAQfQu3dv/vjHP1Zp3d2xYweT\nJk2iS5cudOvWjVtuuSVmy2946n9qaiqHHXYYzz//PAcccAAzZswAYNu2bYwcOZIOHTqQl5fHqFGj\nWLt2LQBz587luOOOq7LP3//+95x++un1ep8bQ61/kVW1TFVnq+owoA3wc2AScANwvffvocD+qnqi\nqs5R1UTacU8HlonI8yKyQUQ+FZErEz4TY4wxjSbRmzw/oNlX0w5jpa2mpqZaWppJSLS+rY0R3CUz\nsNvXH/Q0NwUFBZx99tmMGzeO119/nU2bNpGZmcnYsWOZM2dOqNwLL7zA0KFDad++PZ9++imTJk1i\n5syZbN26lSlTpjB69GgqKipC5Z977jkWLlzI9u3bSU1N5aCDDuLdd99l586d3HbbbZx//vls2LAB\ngEcffZRFixaxfPlyPvnkE15++eUqD8IuvPBCMjIy+Oabb/j000954403eOyxx+I+x5SUFE477TSW\nLl0KuL9FkyZN4rvvvuO7774jOzubqVOnAjB69GhWr17Nl1/uaWOaNWsWF1xwQd3e4EaUUJu6qpao\n6juq+qSq3q+qv/P+XaiqZXWsQ2/gCuBfwHDgAeBeC+6MMab5SqR/nW9fvpmraVqIlJSUffZ9MYmL\n1lLuT/zdkJKZirmvP+ipor8k95Wgd999l7Vr1zJ69Gj69OnDYYcdxuzZswEYP348zz33XKjss88+\ny/jx4wEXiE2ZMoV+/fohIkycOJHMzEw++OADwH3GV199NV27diUzMxOAM888k06dOgEwbtw4+vTp\nw7JlywAXNE6bNo0uXbrQpk0bbrzxxtD1sWHDBhYuXMgf/vAHsrOzOeCAA5g2bVqVusWjc+fObN26\nFYC8vDzGjBlDVlYW++23HzfddBPvvPMOAJmZmYwbN45nnnkGgFWrVrFmzRpGjhyZ8Pvb2JrDdAcp\nwMeqerOqLlfVp4AHAQvsjDGmmaqpf11FRUXUG7amSjtsDiNy1nRTbP0PTbxijUTbGA9Nkj0R+r78\noKc5efrppxk+fDitW7cG4KyzzgqlYw4dOpTi4mKWLVvGt99+y/LlyxkzZgwAa9asYcaMGbRt2zb0\n+uGHH1i3bl1o3927d69yrIKCAo4++uhQ+ZUrV4bSOtevX1+lfLdu3UL/XrNmDRUVFXTu3Dm07WWX\nXcamTZsSOte1a9fSrl07AIqLi5kyZQq9evVi//33Z8iQIezYsSP0t+KCCy7g2WefBVxr3dlnn016\nenpCx2sKiT1ubRjrgC8iln0J9GiCuhhjjKmFf4MX7SYvGAxSVlZGZWUl2dnZVdalpqZSVlbX5I66\nKy0tJTU1lYyMjEY/NtSewpaWlkZJSUmT1c+0HLGuJREJtfwmK10ynuPWh9/KmGjLv0mekpISXnjh\nBYLBIJ07dwZc37rt27fz+eefc+SRRzJu3DjmzJlDhw4dGDVqFK1atQKgR48e3Hzzzdx0000x9x/+\nN2LNmjVceumlvP322wwYMAAR4eijjw4FUp07d+b7778PlQ//d/fu3cnMzGTLli1xT7ETKRgM8uqr\nrzJ8+HAAZsyYwVdffRUaNOazzz7jmGOOCf1969+/PxkZGRQWFjJnzpwqKanNWXP4Nr2HGwkz3MHA\nt41fFWOMMbWprbUuMzOTYDBIaWkpWVlZoXXhw/sn88l/bQKBAKraZIFTTWmYUHWev8Z8X0zLEwgE\nQmltkfwBSRoqsEtWGqYvNTWV0tLSpO6zRfqg6bIJXn75ZdLS0li+fHno/0dVZdy4cRQUFDB9+nTG\njx/PaaedRvv27bnnnntC206ePJkxY8Zw4okn0q9fP4qLi1myZAlDhgxhv/32q3asoqIiRIT27dsT\nDAYpKChg5cqVofXjxo3jgQce4NRTTyUnJ4f77rsv9P9h586dGT58ONdeey133XUXrVq1YvXq1axd\nu5b8/Oqzo4VnaFRWVvL1119z++23s3HjRq699loAdu/eTXZ2Nvvvvz9bt27ljjvuqLafCRMmMHXq\nVDIyMjjhhBPq+C43ruaQivkHoL+I3CQiB4nIWbipEh5u4noZY4yJoqb+dX4Qk5mZiYhUa6Fr7PQr\nv65NmYoZT2uHpWOaeNSW0ttQqc7BYLBBAkab065pFRQUcPHFF9OtWzc6dOhAhw4d6NixI1OnTuXZ\nZ58lGAxy/PHHs99++7F+/XpGjBgR2vbYY49l5syZTJ06lby8PPr06UNBQUHMh1OHHXYY1113HQMG\nDKBTp06sXLmSQYMGhdZPnjyZ4cOHc+SRR3Lsscdy6qmnkpqaGrreCwoKKC8v57DDDiMvL4+zzjor\n5tQFIsLzzz9P69atadOmDaeddhoHHHAAH3/8caiP37Rp0ygpKaF9+/accMIJjBgxolrdJ0yYwKpV\nqzj//PPr9T43JmnqfgcAInIKcA9wCLAG+KOq/jFK0aavrDHG7OOKiopC6Tjh/DTM8BTM0tJSUlJS\nQk+DA4EAFRUVVVryGlJpaWnohjc9Pb1Bbk5roqqUlJSQk5NTY7nKykoqKysb7X0xLY/fCl7TtVRc\nXExWVlbSW9difefry58Me29OQ/Zb5E1iFi5cyOWXX863337bZHUoKSmhY8eOfPrppxx44IFNUoca\nrp+oEXRzaLFDVf+qqn1VNVtVD40R1BljjGliNbUYVFRUVOtcnpWVRSAQCN3ANVWLXVPNmxVvapwN\nJGFqE89ItA1xnTdkUGLz2RlfaWkpf/3rX6msrGTt2rXccccdnHHGGU1apz//+c8cf/zxTRbU1UVz\n6GNnjDGmhagpUKmsrIz65D0rK4uSkhJEhPT0dEQkqUOnxxLeL8gfuCVW/6SGrEM8I6n5/Q8b430x\nLVM811J6enrSB+JpqDRMcKmY1pplwD1AuP322znnnHPIzs5m5MiR3HnnnU1Wn169eiEiVSZRbwnq\nlIopIq1waZOtVHVp0msVm337jTGmCcVK9fJb5SJHwvT5KYkZGRkEg8FQkNeQysrKSElJCR2nuLiY\n7OzsRh2gJJEUtn0hLc3UXVFRETk5ObVev8m+zsvLyxv0+1paWtokadKNxVIxTX00aCqmiHQXkXnA\nduAjYEnYusEi8oWIDE1kn8YYY1qOaJMjA6E+bLGICNnZ2aHBVBoj7TByNMrGnkcv0ZEEbQAVE0tN\nU4xESnZ6Y0NNoeCzdExjkifuvzgi0hn4ABgNLADep2q0+CHQETg7mRU0xhjTPNR0g1fbkP6wJ7ir\nqKigoqKiIaoY4rcKht8IN3bgFE+fqHA2QqCJJZHgyp8fLlkaOj3Y+pcakzyJfFNvwwVuw1V1DPBm\n+EpVLQeWAgOTVz1jjDHNRazgLZGbzpSUFLKzsykvL2/Qp/TR6trYN5DxBLuR7CbXRJPod8yfF7G+\nGmNuRX//lq5oTP0lEtidAsxX1bdrKPMd0KV+VTLGGNMcxbq5rKioSLhlqlWrVuzevbvBWqhiBVWN\n1SpW1wnHLS3NRJNoOmSyWqcbOg3TZw80jEmORB4ldgS+qqVMBVB9unljjDEtmv80PVqgEggEEp5/\nLSMjg8rKytAcb8lsFfDrGi19zA+cGnqAkkTTMH2pqamhQVSM8SX6kCAtLY3y8vI6XYPhEu0nWld+\n+mh969tcNeaATWbflsg3aBvQvZYyfYDo08AbY4xpsWI9uY93nrZIKSkpiAiZmZkUFxcnNbirqU6p\nqamUlpY2eGBX22AysYSnpdnNoIG6tZqlpqYmpWU63uk66sv/Xu6NLMXUNKZEHsO8C4z2BlGpRkT6\nAL8AFiejYsYYY5qPWKmNdQ1goGpwV1JSkrQboJrqlMz+RzWpTwqbpaWZcHW9lpIxCmysUXAbgg0e\nZEz9JfJt/R2QDbwjIiO8fyMi+4nIKbiRMhWYkfRaGmOMaVLRbi5VtV4BjN8PKC0tLTSxcn2paq2j\n+DV04FTfUQSbop9dZWWltSw0U3X9jqWnp9frOvJHlm0sjT0diTF7o7j/8qjqh8ClQC/gNeBX3qod\nuAtFl1QAACAASURBVKCuF3Cxqq5MbhWNMcY0pVg3eLH6kcXbRyz8Ri49PZ20tLR6B3fx9G1r6GkP\n6jIaZrjGbrHzJ4+3VsLmqa4PCup7HQWDwUadNNwGDjKm/hL6n0JVnwCOAB4AlgHfAJ8CDwNHqurs\npNfQGGNMk4oVqEQb7KCyspKysrK4btD8tEhfRkYGKSkp9eprE09Q1dAtA3XtdxiuMdPSysvL6926\nYxpGfVt/6xPcNdaImL7I/w9M47D0171Lwo8UVfUr4L8aoC7GGGOaoUAgQGZmZpVlfspj5I1fWVkZ\nOTk5lJaWxtVq5d94+vvJzMyktLSUsrKyaseMt661jdDpT1zeEAOU+P336tsvyQ8+G3qQF1WlsrKS\nnJwciouLG/RYJnH1Da78VrC67CMQCDT49Rcp8v+DvVFzGRhJVUMP4XJychqtL6VpWPYpGmOMqVG0\nVoNoLWP+DaT/iqcFKFq5rKwsgsFgwsP+J3JD2FBpX3Wd5iBSY6WllZeXk5GRgYjY4BXNUH2DnPq0\nTjdFALK3p2OWlpY2i+lM/PTrlJQUsrKymkWdTHLE/ddHRHrEUSwI7FTVnXWvkjHGmOaipknJI1vG\nysrKyM7OBlxaZTytdqmpqVRUVFRbnp2dTUlJCRUVFXGPuplI3zZ/vrhkD+Ve3/51vvDROxvq5tpv\nrWvVqhXQeHP8mfjVZY7IcOEBeyItMvVNAa2r1NTUOrfWN3cVFRWhPsRNeX6BQIDS0lIyMzND/1eV\nlZU12WdukiuRvz7fej/9BOjwvzRVlonIeuAl4HZV3VyfChpjGo8/SEZzSBMxzUO0NK5o6YZ+OX+Z\nP5VBbS0ONQUwWVlZlJSUICJxBUuJBCXJmucrUrS01bry09IaatJmv7XO5w9eY4Fd85CsoN6f/DuR\n67KxJiaP1JBp0k0pEAiwY8cOcnNz2b17d9IeACXK7wOdnZ1d5fPNzMykvLy8Xg8RTPOQyLe2AHgH\nF7zt9P79gvdzp7f8HeCvQCVwBfCRiByQzAobYxpOeXm5pWSYKqIFFtHmiSsrK6sWEGRmZlJWVlbr\nMWIFWSJCdnY25eXltaZn+U+bE7kZTElJSepIkMl+MNKQo3f6rXXhn6N/Q20DWDQPyeprVpf0xqbs\n57a3TXsQCATYvn07rVu3JiMjg+zsbIqKihq9Hv7f92j96fz/aywVu+VLJLC7FzgKuA/opqo/V9Vz\nVPXnQHfgfm/9tUBv4A6gB3BTTTsVkdtFJBjxWleXkzHG1E8gENir/qCa+onVkhY5GmZka50vvNWu\nJjXdePrBXVlZWY37iTZCZ22SHTgl+yl8Q97gRrbW+fb2Pk4tSbKCK/9hQyI37Y091UG4hp6OpDFV\nVlZSVFREVlZWqMU0OzubioqKRnuA4venCwaD5OTkxHzw5LfamZYtkcDut8DnqnqjqlZ51KCqu1X1\nBuBz4D5VDQB3AsuBkXHs+0ugU9jrpwnUyxiTBOGtDfbUzkD01jr/ZiQ8iIvWWufLyMio9WahtiHZ\n/eCutLQ0Zrm6pCwmO3BKdtpkeFpaMkVrrfNZYNd8JLPVLNHPtSlTIRt7HseGUl5eTllZGSkpKeTk\n5FRZl5WV1Sij0AaDQUpKSkhLS6s1zdJa7fYOiQR2+cB7tZT5P2AIgLq/RB/gWvNqE1DVjWGvLQnU\nyxiTBP5N6d70tNTUT7T+dfG21vlSU1NDUyPEEk8KYEpKSii4i9yX/3uiN6Lh/fuSoSEGH2iIQKum\nQWMaqu+hSUyyB85J5DpqDoNotPQRWv0MAxEhKyur2ufoZyE0ZKtdIBCgpKSEzMzMuAeJag6tdqWl\npfZwqR4S+eZmAZ1rKdMJCO+duxvX3642vUVkrYj8W0TmiMhPEqiXMSYJ/Bt0v6O9MdFaDCLTDWtq\nrfPF09cungcK/tDcflpRrDolIlmtAw3VJynZrYo1tdaFH9Me7jStZKdC+oFaPIFEc5hHrqW2HPtp\nj+DOISUlJer/Tf7DsHj6INdFZWUlpaWlZGdnJ/RZpqWlEQwGmzSoDgQCDR707s0SCew+A8aJSNQ0\nSRE5EhjnlfP1BDbVst8PgAuAk4HJuODw/0QkL4G6GWPqyb+RSHYrhmmZog0E4v+x928Sa2ut88XT\nahdvMJGamhoK7vxrtD6BXbJuIBtqlLtkt6D5rXU1tQS11JvqvUlDBFfxfq7NIbBriQOohKc9pqen\nU15eXuNIpP7AUMn+W1tWVhZzkJR4xJM+31D8ay89Pd0eMNdRIp/4nUAOsExEHhORC0VkhPfzcWAZ\n8P/svWmUXdd5Jbbvm+dXrwoDSYEYKAIEBEogTUohKVGMZC9FluRJsmzZXnZ7zEq6s9RxrAydOLG7\nk15OupNeSXdip7udtiNPlOleialIpgU3ZWq1LIpTEwJAgIAoApwLNbx5Hm5+FPZX3zt17333vvcK\nBRJ3r1ULQOEN595z7jnfsL/9pQH89wBgWVYGG86aJ33Ttu3HbNv+M9u2z9i2/W8AfPLquP7WFNcT\nIkSIKWBSb0I6Zgg3Ncyg2TpikrEQxJCLRqNIJpOSuTNbLwTBvLJTTrTVeWFe6p1+snXA29Oofqdh\nO9aTXzbGTrU60JhHgPFaBijp1JH22Ol0HCmYGtxL5+VE6Wyhl0jKJOxk1o5nTCKRQL/ff1vTcXcK\nvsOLtm3/pWVZPw3gdwD84tUfjSqAX7Rt+7Gr/44D+ElsCKP4hm3bLcuyzgK4Pcj7QoQIMT1MI4IG\nwE702QlxfcCpJ1y/3xcRAL/ZOiIWi3k2wQ3av4prs9FozNR3TQsGTWvMBm1z4NQH0AsMtMxq6LPZ\n+6RxzuOehJgNswQr3OCn6T0doWstnKKdMP7dtm0RH9Fjc3qtE7if6Ebc2wGzN1yv10MkEpn4vEaj\nUUSjUfT7fSQSiZnuOR3LRCLhu57OCwzEXeu+doPBALVaDaVSKeytNyUCrXTbth+2LOsrAH4EwN0A\nitjoYfccgD+3bbuuXlsF8JjjB3nAsqwUgGMAHg/63hAhQkyHwWCAVCqFwWAAy7IQjUbR6XR2elgh\ndhBmjY/pvNCQCYJJB3XQhtx83azZJTpO0xrSk8Zs2zaGw6G0E6HBmc1mfY9v1sbhtm2POeZ+vvN6\nyNzciNhOh5rz6rZeZ6nt4xrTThf/NH+nwT1F7y+WZSESiUhjdf5b/7/5HrcxtVotRCKRbbmn/X5f\nnisGQ6Z5zibRNr0wHA4lQzjN3DkFjWKxGHq93jUN7oxGI6ysrOCLX/wivvCFLyCTyaDX610X1OC3\nEwKHMK46b3949WdmWJb1PwN4FMCrAPYA+G+xQen8v+fx+SFChJgMRoc7nY5EGqlKFhp2Nx6cDHpm\ne4Dg2TqCWTu3jME0mWLWY9CwmQbRaNRTKXISBoPBFqNMO3K2bYswEWtHms2mRPgnQauGThvVn5St\n433n/3Ou5hH9DxEM20nrpcHu9ozN4swPBgOMRiP5bNNRC7p26ZTNmpFPJpPodDq+nS2/IAMhnU7L\ntfmhYGrE43EMBgNhSAS9R/1+H71ez/deYoItGbLZ7Jb3JxKJqQJ402I4HOL555/H+973PgyHQ8m2\ndrvduc/dOxnXg8X2LgB/gg3K5r8G0AZwn23br+7oqEKEuEHAaBhpL8zUhQIKNy4m1dcFqa0z4VVr\nF7TejQYwDaJps8yzCpTwvf1+H+12G81mE71eT6TOs9ksksmk0KUsy0IsFgs03lnqXplJ8XLSut3u\nmCDN211u/u2M7cxQTFrrs2TsmF1j2xwGf3SmLQjm1ceRY9mOWjbt1PV6PaFX+gXvDR3uIOh2uxgM\nBlOLpDDwxNYLJmKx2ETRq3mi1+uNOXZ8DiKRSGiLBEDgjN1VquT7AdyC8dYGAtu2v+j382zb/qmg\nYwgRIsT8QOOYETJu5POgf4V4e4LUXIKZW8uyps7WEfF4HM1m0zU6HaS2SwuBMCrf7XanojRRoMSv\nUUZ6ZbfbRbfbFeMsmUyOjd22bfR6PXGsSNmKRqOoVCpjhqEXKGgyTa3QpGwdP5cUbEboZ/nOENNj\n3q0OTHjN63A4nCrzzZ5t867N41hnzRwnk0m0Wq3AjpcJOnXxeHxsTEEpmBp0oPxm7TiGaDQ6dTZt\nNBrJs866QKd9lxmz7c7a2baNc+fOIZ/P49ChQ2i1Wuh0OsjlckgkEqI2GmIyAt0ly7J+CcA/AlDy\neJkNwLdjFyJEiJ3FcDhEIpFAp9NBPB7HcDiUyCuAuTbJDQoaxdPWHoQIDidhD53tmcch7yUFHqS2\nyzRC2Qah1+sFDkj4EShhhJuZs2g0CsuyUCgUHI0O0qRisdgWlTpSM7vdri9DOhqNTtXzyk9tHQ1n\nZnPoHDNrHxpU1w6sZd1OuM3rrFTf7QgCkp49D0ow94dpFSO18qV574JSMDWYvSdN2+s+eo0hCDqd\nzlgQyk0sRbeq2c6yjG63i+effx733nsvLMtCOp3GH/3RH+GHf/iHUSwW5cwIA82T4XuWLMv6OIB/\nCeANAF+4+us/B/DfAPja1X//GbaqZYYIEeI6ha7b0RRM/n2nZc+pNhb21Lt2cGtKTvW2WbJ1BOtK\nnObVLx3TzQlLp9MYDAaBeyA5UY9HoxF6vR5arRaazaZcfzqdRiaTEcfU6X41m02MRiN5nZPBl0wm\n0e/3fV2vVqoMAj9KmPpekjLKaw3bnlxbbHe2DnCn9U5rvDN7vR3jnucajEQiEsQMisFggHa7jXQ6\nvcWh6na7M2UCec+5x7qdd8PhEO12G6lUaianjuPVn+HV4oBZu+0C722pVMK9994LYFPB9bnnngMA\ncXpDW2AygjzBvwZgHcAHbdv+J1d/9+9s2/4t27Y/jo3m4p8G8NKcxxgiRIhtAg9jbug0HmkU0wDf\nKTBzGDYqvXYw6+uYPbMsy3fEVNdqOcGyLNcGtH4NOa9MUjqdRr/fD7R26TT1ej10Oh00m00xZlgn\nR4OKTpIpDU+HjnUrbg6dvlZm7fwgaN2rn9o6J6GOVCol1KxpnMkQ02M7hVM0nJ6zaZ2zeWXU3DDP\nek8GOYKcKcy8O9WysRZsVlYJnW23fbHf7wtbYpb1QcaB03jd6p911m7eGAwG0h7iE5/4xJhS8F13\n3YXTp0/LPkQxlxDeCOLYfR+AL9u2XXN6v23b/xc2mpH/+pzGFiJEiG0GjXhurHTsWGi+kwIKzCbu\ntHN5o8E0LGm0+c3WURmP4gJu8IrA+nEmvAxgUnm63a6nk8i6FjpyVKeLx+PIZrNIp9NIJBKu16zr\nU1utljh0qVTKV+aDtTWM1Pt5fZDshZ9snVP9Eu9fp9PZ8az99YRrkS24VtLuTkGC69Wxm7eQF9uu\n+DnbKFDiVAtr23ZgNV62RzDBc85pX+x0OjKGWdgSpFm7UemvddaOjByyBMyg4bFjx3DlyhWsrKwA\n2LhHO9U4/e2EICskiw0aJtEBUDBe8wyAD8w6qBAhQlwb0DClrDnFJ0w65k7QsXjI8SALN/PtB2m5\n2oDRzr+fbF2/35d+Sl6GAMVGnAy2SQ6Mn4bg2jnRn0XBk1arhVarJdeXyWSQy+V8NRYmer2e/KRS\nKd8OnR4jsFlzOMlxCBJo8ZOtA8az9uZ3pVIpV0P0RsNgMECj0dhW5+5aNgd3ctinoWJy/WznmOcd\nXKBarRcl0035UoMtUvzeMzqJTk6lZgFo5449+PyKLHnBqQ6Q38G1NylrNy9bgPtwJpPBYDBw3Hfj\n8TiOHj2KM2fOyPdSJCuEO4I8wcsAdqt/vwXgDuM1BUyhtBkiRIhrD10oz42VRhz/btv2jrU90P3M\nwtYL1wZmFoxGG+dikgFDuk40GkUymRSxETe4GRGT5tuvSh4Nok6nI20IuL7T6bS0ISC90m8QgxlJ\nCjHMEkmnU+U3Iu53jGZfOifojCMzluZ30QC+kQMrNMaZ6dkuXMtGzGz4zXmdVjhllv6PfsF6q2mc\nareghBcNmk4dVW6dQAqmXzEPOiOkaDs5J3To4vE4ut2uqAfPQzCEAVunemCKlAGbQTW3rN081j8b\nqtNZpZCLE06cOIELFy6MBZqpzhzCGUFOorMYd+S+AeD7Lcv6MABYlvVeAD9x9XUhQoS4zqGNdjpy\n/D0jt6TfXetNVPfSAna+1u96xjxrH8z6OhoZQbJ12shLpVLSxNcJzNqZxtekzFQQpUY6cclkUurk\n3OiJkwRK6NCRopjNZmc2xLVC4Wg0mui0+QlyMFs3ac7o2LF5s1svq0QigVarNfli3oFgZoG0XDfR\nn3l917VUINVraRqnUgdy+O/t2qenYY6QgeI2pkQisSX4NBqNpCm6m8MahIKps250XhgkM/c9zofu\n4TaP9cDPdNoP+v2+CE5x33Nz4OaRtdNOHR12rcJt4j3veQ8+97nPjfUh3G4xl7c7gjh2XwXwQcuy\nbrn6738MYATgry3LWgFwCkAewP8w3yGGCBFiO0BjkjRMZkF0PRX7c11rAQXTcKcRfiNnDdzQ7/fn\nRk0xjTs6+ZMyP4SZSSOdz2t8blk7N+duGroamyT7gRMNlD2fKEnO1gHzMLq0wTrpXpmvd4OfbB2w\nmYmiIIPbZ5MutZ3ZKhMUi9hJmJmF0WgkgY7t+r7tlJQ3MatjZwZy6AQ3m80tFOh5jDXI53U6HViW\nhUwm4zlfDD7RYXFTvtTwS8HUTqLpVNF50kECy7JkzRUKBYxGo5mDCF51ddxfuU/zeduurJ3p1AHA\n+fPn8dhjj7nuVdFoFJlMZkwxNBKJTNXQ/UZBkB3knwPYB2AVAGzbPgvgo9hw+NYA/CWAH7Rt+yvz\nHmSIECHmDx7kFEmhgUdFQR4yNOyvZcZM0zAJN7WwGx3MIMzDANCGCrM5frN1bnVvk5QfSYF0ogE6\nrbnt7qum1zoj86RlZTIZ+e55joNOrB+DxVTkdILfbB3rIXUvK6d5ikajEvy5Vs9gUFXTecNs4EzF\nU66P7cjamf0jtxs6YDaNU2k6doPBQBRkuY6bzaav+tFJCMIcabfbsCxrrE+bW1CQmbR6vS4OkNd9\n8EvBZHbfrTUBx6cDOZ1OR/YA/jnL86Yzi06Ok54/OvW8x/PO2pnPE/H000/jlltu8Xjn5p6sM3WJ\nRCJsf+AC30+xbdt927bfsm27p373pG3bn7Jt+6ht2z9o2/Zfbs8wQ4QIMU/QCB8Oh/InsLFZptPp\nsUghDdhrZWSZNEwirLPbChorQaPZTjDr63iv/WbrvIQ6nChP5v+bRoTbNTk5/fNEJBIZE1gh5VJ/\np+7/OA/otU2DxSs77ZW185utazQaInJD8Jlz+mz2AOv1etsupqT3gJ3I0mujnHQxMhuYtZu3g3ut\ns3WEdlSDfD/3C1NoiespFotJv0dgw9micuw0YNDIy5BnbRxrfIlJVH4GMCep/vqlYOrMn1cWlHtK\nv98XumY2mx07j2dxXtzq6gjN2tF1vqyt98raBcmm83kynbpOp4MXX3wR73//+z3fzzpk/km7xY3t\ncaPj2u8iIUKE2HGwlqPT6UiUXhuD3NSZyZuleD0o3DIhZrF/iE1nah5Or77vjMgGEQeYlMFi+wGn\n+SNVUl+D01xzDW6XAUwRAWYYstmso7M671oonY1gJN/LcPKabz8ZVi3kYMItUs89gWI02/kcci3t\nRDCHzoF2einuoMUt5p0tuJbCKRrMCgUNUjjRMJ3GTwM8k8kglUqJUM80VE2v9aCdOnP9u2W+tPJl\nsViUWjQ3dLtdz/YnwCY13inzR1pkq9VCt9uV+vZKpYJ4PI5EIiHnHJ2XabN2dNbd9gKe79znSTXX\ntX9eWTsdEPaCGSTReO6553DgwAHk8/mx3zsFj7j/aMpoPB53dT5vZPg+HS3LOmRZ1icsy8qp38Us\ny/oHlmWdsizrW5ZlfXp7hhkiRIh5Qrc5oFGZTqfFqM1ms7K508ifR1bID7wyMrNSU95poAE8j5YU\n2mEaDocStfVbWzfJKJ0kMe4UfTWva7vEJejQUSAkl8tNVJOc5zjMwAk/221O3ebbT7aORpNb1sGt\nGbEO9qRSqYlN6GcBr+NaO3Y09JPJpKxnOj18zsh2mPdedK2FUwga90GCJWR0mP0uJ42ftMdpqZpu\ndEzOG50jE06Kt2Z2T7dHcRoLHSUvBdBer4d+v7+lkTlpiO12W54f1oyR3luv19FqtWR/5DiYtQsC\nOpBemUUdoEilUshkMpLlrNVqEux1c5z8NAvXTp3T+fDss8/i3nvv3fIeOsfmPJw7d06CgNr53Ola\n3OsNQcKe/x2AP8BG/zri16/+vBfAvwfgS5Zl3Te/4YUIEWI7QOOExiRpGE888QSefPJJiawOBgNY\nliWH9nYbWW40TCKkY26CNVk04mmgTQMz2s5shN9snZ+aLmCz3s7JueO16GswjbHtoGGSBgVsiIRQ\nDc/rXm5HdsUMnEzq1+SU0ZyUrWPd2iS1PScnW9f20Rie1IR+GuisrJ96wnl+b7vdRiKRkHszGo2k\nzQFB52Iag9sL0/SQmxcmURxNONGugzqmTlTNdrvtub879Vuk6qSXiiWAMdEbN1ET0o1NR4FUXDfl\nRgCSxc5kMmO1i1TRjcViwgDQz26pVEKpVEIqlZJrYNCV9NV+v496vY5erzemXukEnXF2C/AwWMuA\nEtddNBpFOp1GoVCQ9jAApsraOWW+zfvV7Xbx3ve+d8vvqVys5yEajeJLX/oSqtXqmPAMP3sneu1e\nrwiyi9wP4HHbtgcAYFlWBMDfBvAigP3YaEzeAvCfzXuQIUKEmB9oQJBWQmEI27bxzDPP4G/+5m/G\nDmkeAteiUfmkTIimqdzoMJ2cWZxek4ZJB8FPti4oPTKRSLjKoptGlRmh19Lqs4LRchpj+np10MPE\ndhng5rVOElIxXz8pWzccDkXafJIRTifTvH69xphRm3ezYHMPuFbtVqh4qr/bSXiC9VrzzNox0LZT\nCOrYmXM0S32gpmqy96UXVVM7RXTQ2IvSC3QKB4OBp6hJPB7fsj/RqXO6RjowZCQAmzV2LHXQokus\n0zOdQFIM6VxyT8pmsygUCnKvSJtsNptoNptot9tjDh9rQb32ST7/bnXRiUQCyWRS6MeNRsORfu2W\ntaOz7ebU8Vp++Zd/eYv4DrOriURirP1LLBbD0aNHcerUqS31dWHT8nEEeRL3Arik/n0XgF0A/g/b\ntl+zbfsZAH8O4F6H94YIEeI6ge5dRSMlkUjg9ddfF4rNd7/7XWQyGYnwsyHodjt3fjIyYU+7DZjG\n1SyOnc5A0UAIkq0LmkVz629nRl8ZUea45uHUUd1wOByKMelkVLtRj+c1DhNOz5aXkIo5317ZOlLB\nUqmUb+fYSdbf/E5+3zwFDExj81pk6UnJ09/b6/WkBYSGnqd5iTfsFA2T8ApkmHASTTHbnEwLP1RN\nrodJqpNuaDabE0VN9P5ECqbTd5iNzAeDAVqtljyLlOnXILXXpElGIpGxZ06fc3T2gA0nJpPJIJvN\njgWk6BAzw9dut6WOzwzSsF2DVyCVFMdEIoFisThWh8f9yClr50RndoJTCwYzK0rqPsd+4sQJfOc7\n35H7o3vuhmUamwji2MUB6Kf+Q1f/fFz97jUA3rqlIUKE2FGYDhoFEb7zne/g0KFDOHr0KJ5++mnJ\nrLCIe7vpmJNomMRONEy/3mDSMIHJzbXdYPaFm0TjMeGlhukGGjZOdVpmwT7X6qx1bcPhUFT50un0\nxGt0W+vb1W7Baf68hFR0XZ5Xts5UpPPriNPw0/PjRP9MpVIYDAZzeSadsr9O9Lt5goIR2ilmdtON\nesc16daqIyh2SjgFwJhh7Cdo5xRA2I5gh0nVbLVasl/0ej1Pmp8T2JOVrQS8oPcnt1o1Tee0LEv2\nllQq5ek4MpDqBKoHOzFk3OjRDEiQ4lkqlZDNZsWx0vXDdPx6vZ7cDzdoxgD393Q6jXg8jk6nI9er\n9ycnOrMT6DCbgRTOjbYFdFbw2LFjeOutt1CtVgGMZ+p4f8L2B8Ecu9cBvE/9+wcBrNq2/YL63R4A\ntXkMLESIENsD0kwoc0yJ5xdffBEHDhzA7bffjhdffFE2WtIhaABsl1Pl1+B0KoS/0aCdKd1bbBrH\nWxuVNAImyXnr95oOpl/QIDGdFlO8g9c0S1aj2+3KdTmps7mNz1xj263K6TR/um+e0xhZB+ZkLOo6\nFy2M4/c5c4qCO90XKp7Oo+WG09i263nn2tMOHKlyToqhhNmeYlbxhp1qdcDvZs3lpL1DN7TWv9Ot\nCOY9T6Td0VkZDoeoVCqOPTOdoJUvKWriZ4ymUq0GqZY8Q+nQTdpbeJZ6fb92lPSceFF/eY3pdHqs\n5joWi0kLo2w2i2w2K/WxdBLb7Tbq9bqjQ6QZA3Sc2DCcAZ12uy2BNz9OHbBxfunWEgxOJRIJDAYD\nVCoVmTNNjdV0TADyGWQVObEMbkQE2Um+DOBjlmX9L5Zl/UMAHwPwqPGawwAuTzsYy7L+nmVZI8uy\n/tm0nxEiRAh38BBnFJI1AKurq6jVati/fz927dqFm2++Gc8++6xQtxi1Y5R6OyLoTkYd+4mZuNFp\nF1rVlCqH7LkW1LHT950UmlmydbZto9Fo+FojzAqbc6mj02ySPq3hS6NgUuNhJ5hrfbszK25ZE91f\nynw9aYROc0ZDS1Ncg4zfSSDEyQHQioKz7A1ulL7tYAr0ej2MRqMtQQzudV5rRT9nkUhkpqwd+yHu\nVI0d14Qf59npeeecjUYjNBoNVKtVqV2dNxjwWVpakjrxSaqajUZjTM3Sbw9CZmxNqiGFTHh9dHL8\n7C2DwQBf+cpX8Mgjj7i+hnPBwKoeqxv1l+e43zFQpIVU9H6/j0ajseW1mjFgZvCp7km66fLy8hal\nVDeQ4gls1uMxA9pqtRCPx9Fut+Uekxpr2zYefPBB7N69Wz5L7410DG/09gdBTrl/DOB7AH4VwN8D\n8AaA3+B/Wpa1F8ADAL4xzUCuqmn+CoDvYJzyGSJEiDlhMBgIHYt1c6lUCqdOncKBAwcAbEQVq+cH\nAgAAIABJREFU77jjDjzzzDNyWPD120XHdKJhDgYDKaB3k16/EcHrZsE+DYtEIiH3KwgdRRv77XZb\nqE+ToNtgaNAg8VvMnkqlxMgmmClmNmAW+qOfvm5uMNf6dtEwCbc+jWb9DcFaWafroxKfKU4QZPxO\nVEM3J2bWNgg02p2M03nTr1k7bGblJvVu5LNnijjNUmu3kzRMYFwMaJJz57R+mC1ptVqwbRv5fB6j\n0QiVSsW1dcA0YI0X6YAMJphUTbPulNkh7QRScMTNAdBqqAyAjkYjNJtNVKtVpNNp5HK5QEEwALhy\n5QrOnDmDs2fPeq4XBlQYWNJ0edPZ4+v80OGZGdZ00F6vJz38qA6sQWE1Phduarn5fB6RSERaNrjd\n27Nnz+Ls2bPi0HFOeI7FYjHkcjlx7vgdpF0eOHAA+/fvH7snem8M2x8EcOxs217GBhXzR67+vMe2\n7dfVS5YA/OcA/mXQQViWVQTwhwB+AUA56PtDhAjhD1qUglmQaDSK8+fP4+DBg+j1euh0Ojh48CDK\n5TKWl5clGqvfO2/HzqRh9vt92ezdjKYblY5J4yWZTI4ZoKxJYfbOD7Qan5tYhBucjDxmx6gg52ed\nuNXb6Xmf1jjkeKYVdjADCNfCCHdb107RaI7HNC553+ZRC2U+f161nFSzm6YNghdFdNr6USeQumw6\ndaRgutGQadibFGHAuVWHX+y0Y8eMIeDNhODzrtcas+3cOyhTn8/nUSgU0Ol0UK1WZ2ZXMMjHrLtJ\nkyRVk88IVTWbzaZQOElHTKfT4jjR2dMNwyk0RKeN13vlyhUMh0MsLi4GduiAjfXzxBNP4L777sO+\nfftw/vx519fqnp9OgkV8HofD4ZZ2HF7QgVkGz5hlKxQK0vrF3G85Fqe6W1JSc7kcbNsW4Rv27dO1\n/J1OB1//+tdlHKyRLBQKiEQi8vwxKEgHGxinpJv7st4btSN6oyIQL8W27ZZt21+++lM3/u8F27b/\nV9u23VerO/4FgEds234CwM5p/oYI8Q4G64OoPEWaVqPRwJUrV3Dw4EFEIhGhVhw5cgTf+ta3xChm\nA1hmhOZZpKydBEo3s17ATXrdL6XmnQIant1uF/l83tEYjEQiyOfz4txNMoT1fW+1Wp51RSacaFn8\nnabw+FkndAh0pJXzrqleQTFLtg7YKq1+LShzXhlxs8E7leXMtgfMxGt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2KZFI4N5778XD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NE5N3hd4K03didoZy6obPUk8CBnPcd2qGTRMQ2aBTQzZGZtHY1h0hYnQWfknBqSezmeNKa8BE24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\n5x2GhoZ033336c4771RnZ6eKxclLYltbWzUyMqJDhw5p48aN5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ZcQBJ2LcjrT73irIwcA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qtVVCWE2MAQpK8f9XAYhUoVufIqGuEIViGIDgwhHO9HuQHkyxUMjo4hOZHCaGoK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\nw8PDw8PjKsf/B/z2LSBgeWtAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d596650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.tools as tls\n",
    "fig = plt.figure(figsize=[15,4])\n",
    "ax1 = plt.subplot(111)\n",
    "\n",
    "recent_color = gray_med\n",
    "avg_day_color = red_orange\n",
    "weekend_linestyle = 'dashed'\n",
    "\n",
    "for row in range(len(hourly_array)):\n",
    "    hourly_array.ix[row].plot(color=gray_light,alpha=.2,linewidth=0.75)\n",
    "\n",
    "for row in range(len(hourly_array)-7, len(hourly_array)):\n",
    "    if (hourly_array.ix[row].name.dayofweek == 5) | (hourly_array.ix[row].name.dayofweek==6):\n",
    "        hourly_array.ix[row].plot(linestyle=weekend_linestyle, marker='',color=recent_color, linewidth=1)\n",
    "    else:\n",
    "        hourly_array.ix[row].plot(marker='',color=recent_color, linewidth=1)\n",
    "    \n",
    "plot_avg_all = average_day.plot(marker='', color=avg_day_color, linewidth=3, grid='off', label='Average Day')\n",
    "\n",
    "plt.ylim(ymin=0,ymax=8)\n",
    "plt.xlim(xmax=23)\n",
    "plt.xticks(np.arange(0,23,2),fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel('Hour of Day',fontsize=16)\n",
    "\n",
    "ylabel('Electricity Usage (kWh)',fontsize=20)\n",
    "title('Daily Electricity Consumption \\n %s through %s'\n",
    "      %(hourly_array.ix[0].name.strftime('%B %d, %Y'),\n",
    "        hourly_array.ix[len(hourly_array)-1].name.strftime('%B %d, %Y')),\n",
    "fontsize=16)\n",
    "\n",
    "# manually building legend\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "recent_weekday_line = mlines.Line2D([], [], \n",
    "                                    color=recent_color, \n",
    "                                    marker='', \n",
    "                                    label='Recent Weekday')\n",
    "recent_weekend_line = mlines.Line2D([], [], \n",
    "                                    linestyle=weekend_linestyle,\n",
    "                                    color=recent_color, \n",
    "                                    marker='', \n",
    "                                    label='Recent Weekend')\n",
    "avg_day_line = mlines.Line2D([], [],\n",
    "                                    color=avg_day_color, \n",
    "                                    marker='', \n",
    "                                    linewidth=3,\n",
    "                                    label='Average Day')\n",
    "\n",
    "plt.legend(handles=[recent_weekday_line, recent_weekend_line, avg_day_line], loc='upper right')\n",
    "\n",
    "ax1.spines[\"top\"].set_visible(False)  \n",
    "ax1.spines[\"bottom\"].set_visible(False)  \n",
    "ax1.spines[\"right\"].set_visible(False)  \n",
    "ax1.spines[\"left\"].set_visible(False)\n",
    "ax1.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\",\n",
    "               labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\",labelsize=14)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('plots/Days_thru_28-Feb-2015.png',bbox_inches='tight')\n",
    "#plot_url = py.plot_mpl(fig, update=update)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
